U.S. patent application number 17/403315 was filed with the patent office on 2022-02-03 for recognition method and device for a target perception data.
The applicant listed for this patent is Huawei Technologies Co., Ltd.. Invention is credited to Jia Chen, Liangwei Wang.
Application Number | 20220036142 17/403315 |
Document ID | / |
Family ID | 1000005913785 |
Filed Date | 2022-02-03 |
United States Patent
Application |
20220036142 |
Kind Code |
A1 |
Wang; Liangwei ; et
al. |
February 3, 2022 |
Recognition method and device for a target perception data
Abstract
A data processing method and device includes obtaining target
perception data, where the target perception data is any type of
the following data, such as image data, video data, or voice data,
determining a target scenario to which the target perception data
belongs, determining a target perception model corresponding to the
target scenario, and computing a recognition result of the target
perception data according to the target perception model. A
scenario to which perception data belongs is determined, and a
recognition result of the perception data is obtained through
computation using a perception model corresponding to the
scenario.
Inventors: |
Wang; Liangwei; (Shenzhen,
CN) ; Chen; Jia; (Shenzhen, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Huawei Technologies Co., Ltd. |
Shenzhen |
|
CN |
|
|
Family ID: |
1000005913785 |
Appl. No.: |
17/403315 |
Filed: |
August 16, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16586209 |
Sep 27, 2019 |
11093806 |
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17403315 |
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15460339 |
Mar 16, 2017 |
10452962 |
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16586209 |
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PCT/CN2015/088832 |
Sep 2, 2015 |
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15460339 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06V 2201/07 20220101;
G06V 20/41 20220101; G06V 20/00 20220101; G10L 15/063 20130101;
G06K 9/6228 20130101; G06V 30/194 20220101 |
International
Class: |
G06K 9/66 20060101
G06K009/66; G06K 9/62 20060101 G06K009/62; G06K 9/00 20060101
G06K009/00; G10L 15/06 20060101 G10L015/06 |
Foreign Application Data
Date |
Code |
Application Number |
Sep 16, 2014 |
CN |
201410471480.X |
Claims
1. A data processing method, comprising: obtaining target
perception data, wherein the target perception data comprises at
least one of image data, video data, or voice data; determining a
target scenario associated with the target perception data;
determining a target perception model corresponding to the target
scenario; and computing a recognition result of the target
perception data according to the target perception model.
2. The data processing method of claim 1, further comprising:
performing a scenario analysis on the target perception data; and
further determining the target scenario based on the scenario
analysis.
3. The data processing method of claim 2, wherein the target
perception data is associated with a location at which a terminal
is currently located, and wherein the data processing method
further comprises performing the scenario analysis on the target
perception data according to positioning information of the
location at which the terminal is currently located.
4. The data processing method of claim 1, further comprising:
sending, to a server, a first request for a scenario associated
with the target perception data; and receiving the target scenario
from the server in response to sending the first request.
5. The data processing method of claim 1, further comprising
further determining, from a pre-stored perception model library,
the target perception model corresponding to the target scenario,
wherein each perception model in the pre-stored perception model
library corresponds to a scenario.
6. The data processing method of claim 5, wherein before obtaining
the target perception data, the method further comprises updating
the pre-stored perception model library according to a historical
scenario sequence of a user, wherein the pre-stored perception
model library, after updating, comprises the target perception
model.
7. The data processing method of claim 1, further comprising:
sending, to a server, a second request for a perception model
corresponding to the target scenario when a pre-stored perception
model library does not include the perception model, wherein each
perception model in the pre-stored perception model library
corresponds to a scenario; and receiving, from the server, the
target perception model in response to sending the second
request.
8. A data processing method, comprising: receiving, from a
terminal, a request message for a perception model corresponding to
a scenario associated with target perception data, wherein the
target perception data comprises at least one of image data, video
data, or voice data; determining, based on the request message, a
target scenario associated with the target perception data;
determining, from a pre-stored perception model library, a target
perception model corresponding to the target scenario, wherein each
model in the pre-stored perception model library corresponds to a
scenario; and sending the target perception model to the terminal
to prompt the terminal to compute a recognition result of the
target perception data according to the target perception
model.
9. The data processing method of claim 8, wherein before receiving
the request message, the method further comprises: obtaining a
perception data sample comprising at least a part of perception
data having scenario annotation information and object annotation
information; training, based on the perception data sample,
perception models respectively corresponding to different
scenarios; and storing, in the pre-stored perception model library,
the perception models, wherein the pre-stored perception model
library comprises the target perception model.
10. The data processing method of claim 8, further comprises:
performing scenario analysis on the target perception data; and
further determining the target scenario based on the scenario
analysis.
11. The data processing method of claim 10, further comprising:
generating the target perception data at a location at which a
terminal is currently located; and performing the scenario analysis
on the target perception data according to positioning information
of the location at which the terminal is currently located.
12. The data processing method of claim 8, further comprising
further determining the target scenario according to an identifier
indicating the target scenario comprised in the request
message.
13. A data processing device, comprising: a memory storing
instructions; and a processor coupled to the memory and configured
to execute the instructions, which cause the processor to be
configured to: obtain target perception data, wherein the target
perception data comprises at least one of image data, video data,
or voice data; determine a target scenario associated with the
target perception data; determine a target perception model
corresponding to the target scenario; and compute a recognition
result of the target perception data according to the target
perception model.
14. The data processing device of claim 13, wherein the
instructions further cause the processor to be configured to:
perform scenario analysis on the target perception data; and
further determine the target scenario based on the scenario
analysis.
15. The data processing device of claim 14, wherein the target
perception data is associated with a location at which a terminal
is currently located, and wherein the instructions further cause
the processor to be configured to perform the scenario analysis on
the target perception data according to positioning information of
the location at which the terminal is currently located.
16. The data processing device of claim 13, wherein the
instructions further cause the processor to be configured to: send,
to a server, a first request for a scenario associated with the
target perception data; and receive the target scenario from the
server in response to sending the first request.
17. The data processing device of claim 13, wherein the
instructions further cause the processor to be configured to
determine, from a pre-stored perception model library, the target
perception model corresponding to the target scenario, wherein each
perception model in the pre-stored perception model library
corresponds to a scenario.
18. The data processing device of claim 17, wherein before the
target perception data is obtained, the instructions further cause
the processor to be configured to update the pre-stored perception
model library according to a historical scenario sequence of a
user, wherein, after the pre-stored perception model library is
updated, the pre-stored perception model library comprises the
target perception model.
19. The data processing device of claim 13, wherein the
instructions further cause the processor to be configured to: send,
to a server, a second request for a perception model corresponding
to the target scenario when a pre-stored perception model library
does not include the perception model; and receive, from the
server, the target perception model in response to sending the
second request.
20. The data processing device of claim 19, wherein each perception
model in the pre-stored perception model library corresponds to a
scenario.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 16/586,209, filed on Sep. 27, 2019, now U.S.
Pat. No. 11,093,806, which is a continuation of U.S. patent
application Ser. No. 15/460,339, filed on Mar. 16, 2017, now U.S.
Pat. No. 10,452,962, which is a continuation of International
Patent Application No. PCT/CN2015/088832, filed on Sep. 2, 2015,
which claims priority to Chinese Patent Application No.
201410471480.X, filed on Sep. 16, 2014. All of the afore-mentioned
patent applications are hereby incorporated by reference in their
entireties.
TECHNICAL FIELD
[0002] Embodiments of the present disclosure relate to the data
processing field, and in particular, to a data processing method
and device.
BACKGROUND
[0003] Terminal devices such as a mobile phone, a wearable device,
and a robot all need to recognize multiple types of objects,
voices, and actions from perception data such as an image, a video,
and a voice. For example, to perform searching by means of photo
taking, a mobile phone needs to first recognize a target object in
a taken photo, and then can search for information related to the
target object. For another example, to perform a task of grabbing a
target object, a robot needs to first obtain a location of the
target object in an ambient environment using camera data.
[0004] A general method for equipping a terminal device with
extensive recognition capabilities is to train, with massive known
sample data, a perception model that can distinguish between
various objects, voices, or actions. The terminal device may
obtain, through computation based on the trained perception model,
a corresponding recognition result for a new image, a video, or a
voice in each input.
[0005] As types that need to be recognized increase, to improve
recognition accuracy, a perception model used to recognize
perception data becomes increasingly complex. For example, a
perception model has a growing quantity of parameters. For example,
currently, a convolutional neural network (CNN) model used for
image recognition already has tens of millions of or even hundreds
of millions of parameters. Currently, to improve user experience, a
perception model in many applications needs to precisely recognize
a large quantity of objects, actions, or voices in various given
scenarios. This poses a great challenge to accuracy of the
perception model. In some approaches, a perception model with fixed
parameters is usually used to complete all recognition tasks, and
therefore, complexity of the perception model infinitely increases
with refinement of a recognition requirement, which poses a great
challenge to storage and computation.
SUMMARY
[0006] Embodiments of the present disclosure provide a data
processing method and device that can resolve a problem of a
contradiction between a computation capability of a device and
complexity of a perception model.
[0007] A first aspect provides a data processing method, where the
method includes obtaining target perception data, where the target
perception data is any type of the following data, image data,
video data, or voice data, determining a target scenario to which
the target perception data belongs, determining a target perception
model corresponding to the target scenario, and computing a
recognition result of the target perception data according to the
target perception model.
[0008] With reference to the first aspect, in a first possible
implementation manner of the first aspect, determining a target
scenario to which the target perception data belongs includes
performing scenario analysis on the target perception data, and
determining the target scenario.
[0009] With reference to the first possible implementation manner
of the first aspect, in a second possible implementation manner of
the first aspect, the target perception data is data generated at a
location at which a terminal is currently located, and the
performing scenario analysis on the target perception data, and
determining the target scenario includes performing scenario
analysis on the target perception data according to positioning
information of the location at which the terminal is currently
located, and determining the target scenario.
[0010] With reference to the first aspect, in a third possible
implementation manner of the first aspect, determining a target
scenario to which the target perception data belongs includes
sending, to a server, a first request used to request a scenario to
which the target perception data belongs, and receiving the target
scenario that is sent by the server according to the first
request.
[0011] With reference to any one of the first aspect, or the first
to the third possible implementation manners of the first aspect,
in a fourth possible implementation manner of the first aspect, the
determining a target perception model corresponding to the target
scenario includes determining, from a pre-stored perception model
library, the target perception model corresponding to the target
scenario, where each perception model in the perception model
library corresponds to a scenario.
[0012] With reference to the fourth possible implementation manner
of the first aspect, in a fifth possible implementation manner of
the first aspect, the method further includes updating the
perception model library according to a historical scenario
sequence of a user, where the updated perception model library
includes the target perception model corresponding to the target
scenario.
[0013] With reference to any one of the first aspect, or the first
to the third possible implementation manners of the first aspect,
in a sixth possible implementation manner of the first aspect,
determining a target perception model corresponding to the target
scenario includes sending, to a server, a second request used to
request the perception model corresponding to the target scenario
when a pre-stored perception model library has no perception model
corresponding to the target scenario, where each perception model
in the perception model library corresponds to a scenario, and
receiving the target perception model that corresponds to the
target scenario and sent by the server according to the second
request.
[0014] A second aspect provides a data processing method, where the
method includes receiving a request message that is sent by a
terminal and used to request a perception model corresponding to a
scenario to which target perception data belongs, where the target
perception data is any type of the following data, image data,
video data, or voice data; determining, according to the request
message, a target scenario to which the target perception data
belongs, determining, from a pre-stored perception model library, a
target perception model corresponding to the target scenario, where
each model in the perception model library corresponds to a
scenario, and sending the target perception model to the terminal
according to the request message such that the terminal computes a
recognition result of the target perception data according to the
target perception model.
[0015] With reference to the second aspect, in a first possible
implementation manner of the second aspect, before receiving a
request message, the method further includes obtaining a perception
data sample, where the perception data sample includes at least a
part of perception data that has scenario annotation information
and object annotation information, training, according to the
perception data sample, perception models respectively
corresponding to different scenarios, and storing, in the
perception model library, the perception models respectively
corresponding to the different scenarios, where the perception
model library includes the target perception model.
[0016] With reference to the second aspect or the first possible
implementation manner of the second aspect, in a second possible
implementation manner of the second aspect, determining, according
to the request message, a target scenario to which the target
perception data belongs includes performing scenario analysis on
the target perception data included in the request message, and
determining the target scenario to which the target perception data
belongs.
[0017] With reference to the second possible implementation manner
of the second aspect, in a third possible implementation manner of
the second aspect, the target perception data is data generated at
a location at which the terminal is currently located, and
determining a target scenario to which the target perception data
belongs includes performing scenario analysis on the target
perception data according to positioning information of the
location at which the terminal is currently located, and
determining the target scenario.
[0018] With reference to the second aspect or the first possible
implementation manner of the second aspect, in a fourth possible
implementation manner of the second aspect, determining, according
to the request message, a target scenario to which the target
perception data belongs includes determining the target scenario
according to an identifier that is included in the request message
and used to indicate the target scenario.
[0019] A third aspect provides a data processing device, where the
device includes an obtaining module configured to obtain target
perception data, where the target perception data is any type of
the following data, image data, video data, or voice data, a first
determining module configured to determine a target scenario to
which the target perception data obtained by the obtaining module
belongs, a second determining module configured to determine a
target perception model corresponding to the target scenario
determined by the first determining module, and a computation
module configured to compute, according to the target perception
model determined by the second determining module, a recognition
result of the target perception data obtained by the obtaining
module.
[0020] With reference to the third aspect, in a first possible
implementation manner of the third aspect, the first determining
module is further configured to perform scenario analysis on the
target perception data, and determine the target scenario.
[0021] With reference to the first possible implementation manner
of the third aspect, in a second possible implementation manner of
the third aspect, the target perception data is data generated at a
location at which a terminal is currently located, and the first
determining module is further configured to perform scenario
analysis on the target perception data according to positioning
information of the location at which the terminal is currently
located, and determine the target scenario.
[0022] With reference to the third aspect, in a third possible
implementation manner of the third aspect, the first determining
module includes a first sending unit configured to send, to a
server, a first request used to request a scenario to which the
target perception data belongs, and a first receiving unit
configured to receive the target scenario that is sent by the
server according to the first request.
[0023] With reference to any one of the third aspect, or the first
to the third possible implementation manners of the third aspect,
in a fourth possible implementation manner of the third aspect, the
second determining module is further configured to determine, from
a pre-stored perception model library, the target perception model
corresponding to the target scenario, where each perception model
in the perception model library corresponds to a scenario.
[0024] With reference to the fourth possible implementation manner
of the third aspect, in a fifth possible implementation manner of
the third aspect, the device further includes an updating module
configured to update the perception model library according to a
historical scenario sequence of a user before the obtaining module
obtains the target perception data, where the updated perception
model library includes the target perception model corresponding to
the target scenario.
[0025] With reference to any one of the third aspect, or the first
to the third possible implementation manners of the third aspect,
in a sixth possible implementation manner of the third aspect, the
second determining module includes a second sending unit configured
to send, to a server, a second request used to request the
perception model corresponding to the target scenario when a
pre-stored perception model library has no perception model
corresponding to the target scenario, where each perception model
in the perception model library corresponds to a scenario, and a
second receiving unit configured to receive the target perception
model that corresponds to the target scenario and sent by the
server according to the second request.
[0026] A fourth aspect provides a data processing device, where the
device includes a receiving module configured to receive a request
message that is sent by a terminal and used to request a perception
model corresponding to a scenario to which target perception data
belongs, where the target perception data is any type of the
following data, image data, video data, or voice data, a first
determining module configured to determine, according to the
request message received by the receiving module, a target scenario
to which the target perception data belongs, a second determining
module configured to determine, from a pre-stored perception model
library, the target perception model corresponding to the target
scenario determined by the first determining module, where each
model in the perception model library corresponds to a scenario,
and a sending module configured to send, to the terminal according
to the request message received by the receiving module, the target
perception model determined by the second determining module such
that the terminal computes a recognition result of the target
perception data according to the target perception model.
[0027] With reference to the fourth aspect, in a first possible
implementation manner of the fourth aspect, the device further
includes an obtaining module configured to obtain a perception data
sample before the receiving module receives the request message,
where the perception data sample includes at least a part of
perception data that has scenario annotation information and object
annotation information, a training module configured to train,
according to the perception data sample, perception models
respectively corresponding to different scenarios, and a storage
module configured to store, in the perception model library, the
perception models that are respectively corresponding to the
different scenarios and obtained through training by the training
module, where the perception model library includes the target
perception model.
[0028] With reference to the fourth aspect or the first possible
implementation manner of the fourth aspect, in a second possible
implementation manner of the fourth aspect, the first determining
module is further configured to perform scenario analysis on the
target perception data included in the request message, and
determine the target scenario to which the target perception data
belongs.
[0029] With reference to the second possible implementation manner
of the fourth aspect, in a third possible implementation manner of
the fourth aspect, the target perception data is data generated at
a location at which the terminal is currently located, and the
first determining module is further configured to perform scenario
analysis on the target perception data according to positioning
information of the location at which the terminal is currently
located, and determine the target scenario.
[0030] With reference to the fourth aspect or the first possible
implementation manner of the fourth aspect, in a fourth possible
implementation manner of the fourth aspect, the first determining
module is further configured to determine the target scenario
according to an identifier that is included in the request message
and used to indicate the target scenario.
[0031] Based on the foregoing technical solutions, according to the
data processing method and device in the embodiments of the present
disclosure, a scenario to which perception data belongs is
determined, and a recognition result of the perception data is
obtained through computation using a perception model corresponding
to the scenario, which can reduce computation complexity and
therefore can improve data processing efficiency in comparison with
the other approaches.
BRIEF DESCRIPTION OF DRAWINGS
[0032] To describe technical solutions in embodiments of the
present disclosure more clearly, the following briefly describes
the accompanying drawings required for describing the embodiments.
The accompanying drawings in the following description show merely
some embodiments of the present disclosure, and a person of
ordinary skill in the art may still derive other drawings from
these accompanying drawings without creative efforts.
[0033] FIG. 1 shows a schematic flowchart of a data processing
method according to an embodiment of the present disclosure.
[0034] FIG. 2 shows a schematic flowchart of a data processing
method according to another embodiment of the present
disclosure.
[0035] FIG. 3 shows a schematic flowchart of perception model
training according to another embodiment of the present
disclosure.
[0036] FIG. 4 shows a schematic block diagram of a data processing
device according to an embodiment of the present disclosure.
[0037] FIG. 5 shows another schematic block diagram of a data
processing device according to an embodiment of the present
disclosure.
[0038] FIG. 6 shows a schematic block diagram of a data processing
device according to another embodiment of the present
disclosure.
[0039] FIG. 7 shows another schematic block diagram of a data
processing device according to another embodiment of the present
disclosure.
[0040] FIG. 8 shows a schematic block diagram of a data processing
device according to an embodiment of the present disclosure.
[0041] FIG. 9 shows a schematic block diagram of a data processing
device according to another embodiment of the present
disclosure.
DESCRIPTION OF EMBODIMENTS
[0042] The following clearly describes the technical solutions in
the embodiments of the present disclosure with reference to the
accompanying drawings in the embodiments of the present disclosure.
The described embodiments are a part rather than all of the
embodiments of the present disclosure. All other embodiments
obtained by a person of ordinary skill in the art based on the
embodiments of the present disclosure without creative efforts
shall fall within the protection scope of the present
disclosure.
[0043] It should be understood that, the technical solutions of the
embodiments of the present disclosure may be applied to various
communications systems such as a Universal Mobile
Telecommunications System (UMTS), a Global System for Mobile
Communications (GSM) system, a Code Division Multiple Access (CDMA)
system, a Wideband Code Division Multiple Access (WCDMA) system, a
general packet radio service (GPRS), a Long Term Evolution (LTE)
system, an LTE frequency division duplex (FDD) system, LTE time
division duplex (TDD), and a Worldwide Interoperability for
Microwave Access (WiMAX) communications system.
[0044] It should be further understood that in the embodiments of
the present disclosure, a terminal may also be referred to as user
equipment (UE), a mobile station (MS), a mobile terminal, or the
like. The terminal may communicate with one or more core networks
using a radio access network (RAN). For example, the terminal may
be a mobile phone (or referred to as a "cellular" phone) or a
computer with a mobile terminal. For example, the terminal may be
further a portable, pocket-sized, handheld, or in-vehicle mobile
apparatus, or a mobile apparatus built in a computer. The mobile
apparatus exchanges voice and/or data with the radio access
network. Further, the terminal may be a mobile phone, a wearable
device, a robot, or the like.
[0045] In other approaches, one perception model with fixed
parameters is generally used to complete all recognition tasks. For
example, recognition tasks are performed, using a same perception
model, on perception data generated in a supermarket, a hospital,
or a kitchen. As types of objects that need to be recognized
increase and a requirement for recognition accuracy becomes
increasingly high, a perception model has a growing quantity of
parameters. For example, currently, a CNN model used to recognize
tens of thousands of objects in images already has tens of millions
of or even hundreds of millions of parameters. This inevitably
greatly increases computation complexity of the perception model,
and also poses a challenge to storage space of the perception
model.
[0046] In view of the foregoing existing problem, the present
disclosure provides a data processing method. In a perception model
training process, a factor of scenario is considered, and
perception models respectively for different scenarios are
generated. In a recognition computation process of perception data,
a scenario to which the perception data belongs is first
determined, then a perception model corresponding to the scenario
is obtained, and finally, a recognition result of the perception
data is computed using the perception model corresponding to the
scenario to which the perception data belongs.
[0047] Types of objects that appear in each scenario are limited.
For example, in an outdoor scenario or a city scenario, an object
such as a person, a vehicle, a building, or a character may need to
be recognized, but basically, there is no need for recognizing
various animals and plants. In other words, a quantity of types of
objects that frequently appear in each scenario and need to be
recognized is relatively small. Correspondingly, a perception model
corresponding to each scenario has a relatively small quantity of
model parameters such that computation complexity of the perception
model corresponding to each scenario is greatly reduced, and a
requirement for storage space is not very high. Therefore,
according to the data processing method proposed in the present
disclosure, complexity and a computation workload of a perception
model can be greatly reduced while data recognition accuracy is
maintained or even improved, which resolves a contradiction between
a terminal computation capability and model complexity in order to
effectively improve a recognition capability.
[0048] To help a person skilled in the art better understand the
technical solutions of the present disclosure, the following uses a
specific example to describe a specific application scenario of the
embodiments of the present disclosure. For example, when a user
takes a photo in a piazza, a mobile phone may be used to recognize
an object such as a flowerbed, a cafe, or a bus according to the
photo. For another example, when a user takes a photo in the woods,
a mobile phone may be used to recognize an object such as clover, a
chrysanthemum, or a mantis according to the photo. That is, the
technical solutions provided in the embodiments of the present
disclosure may be applied to the following scenario in which when a
user uses a mobile phone to take photos in various scenarios, the
mobile phone recognizes various objects in the photos and replies
with names of the objects. It should be understood that the
foregoing mobile phone may be another terminal device.
[0049] FIG. 1 shows a data processing method 100 according to an
embodiment of the present disclosure. The method 100 is performed,
for example, by a terminal. As shown in FIG. 1, the method 100
includes the following steps.
[0050] Step S110: Obtain target perception data, where the target
perception data is any type of the following data, image data,
video data, or voice data.
[0051] Further, the target perception data may be data measured or
generated by a sensing apparatus such as a camera, a microphone, an
infrared sensor, or a depth sensor. For example, the target
perception data is a picture, a video, or a recording.
[0052] It should be understood that the terminal that obtains the
target perception data may also be a device that generates the
target perception data. Optionally, in this embodiment of the
present disclosure, obtaining target perception data in step S110
includes generating the target perception data at a location at
which the terminal is currently located.
[0053] Further, for example, a user uses a mobile phone to take a
photo in scenario A, and directly uses the mobile phone to perform
recognition processing on the taken photo.
[0054] It should be further understood that the terminal that
obtains the target perception data may be a device different from a
device that generates the target perception data. For example, a
user uses a mobile phone to take a photo (the mobile phone
generates perception data), and then uploads the photo to a
notebook computer (the notebook computer obtains the perception
data for subsequent data processing) for corresponding
processing.
[0055] Step S120: Determine a target scenario to which the target
perception data belongs.
[0056] Further, scenario analysis and recognition may be performed
on the target perception data to determine the target scenario to
which the target perception data belongs. Alternatively, a request
for the target scenario to which the target perception data belongs
may be sent to a server. This is not limited in this embodiment of
the present disclosure.
[0057] Optionally, in this embodiment of the present disclosure,
determining a target scenario to which the target perception data
belongs in step S120 includes performing scenario analysis on the
target perception data, and determining the target scenario.
[0058] Further, a scenario recognizer may be used to perform
scenario analysis on the target perception data in order to
determine the target scenario to which the target perception data
belongs. The scenario recognizer may be an existing scenario
classification model, for example, a support vector machine (SVM)
multi-class classifier. Further, for example, the target perception
data is data obtained by a camera. A feature such as a global
feature (GIST), dense scale-invariant feature transform (Dense
SIFT), or a texton histogram (Texton Histograms) of an image is
extracted from the camera data. These features are input to a
previously trained SVM multi-class classifier. Scenario recognition
is performed, and a scenario type is output. For example, it is
recognized that a scenario is "a piazza."
[0059] Further, when the target perception data is perception data
generated at the location at which the terminal is currently
located, the scenario to which the target perception data belongs
may be further restricted according to positioning information of
the location at which the terminal is currently located.
[0060] Optionally, in this embodiment of the present disclosure,
the target perception data is the data generated at the location at
which the terminal is currently located.
[0061] In step S120, performing scenario analysis on the target
perception data, and determining the target scenario includes
performing scenario analysis on the target perception data
according to the positioning information of the location at which
the terminal is currently located, and determining the target
scenario.
[0062] Further, for example, the target perception data is a photo
taken in a piazza A at which the terminal is currently located. An
SVM multi-class classifier performs scenario analysis on the target
perception data, and a recognition result of the scenario analysis
is that a target scenario to which the target perception data
belongs is a piazza. Then, positioning information of the location
at which the terminal is currently located (that is, the piazza A)
is obtained, and the target scenario to which the target perception
data belongs may be further restricted from the piazza to the
piazza A according to the positioning information. For another
example, the target perception data is a photo taken at a kitchen
at which the terminal is currently located. An SVM multi-class
classifier performs scenario analysis on the target perception
data, and a recognition result of the scenario analysis is that a
target scenario to which the target perception data belongs is an
indoor scenario. Then, positioning information of the location at
which the terminal is currently located (that is, the kitchen) is
obtained, and the target scenario to which the target perception
data belongs may be further restricted from the indoor scenario to
the kitchen according to the positioning information.
[0063] It can be learned that, in this embodiment of the present
disclosure, the scenario to which the target perception data
belongs may be further restricted to a smaller spatial-temporal
area according to the positioning information of the terminal. It
should be understood that a perception model corresponding to a
more specific scenario with a relatively small range is
correspondingly more simplified, computation complexity of the
perception model is relatively low, and a computation workload of
subsequent recognition computation of perception data based on the
perception model is also relatively small.
[0064] It should be understood that, in this embodiment of the
present disclosure, a method for obtaining the positioning
information of the location at which the terminal is currently
located may be any one of the following methods or a combination of
multiple methods, a method of using WI-FI for positioning, and a
method of using a simultaneous localization and mapping (SLAM)
function for positioning. The method of using WI-FI for positioning
is further as follows. The terminal scans and searches for a signal
of a surrounding WI-FI wireless access point, to obtain a media
access control (MAC) address. Generally, the wireless access point
does not move in a specific time period. Therefore, the terminal
may report the MAC address to a location server. The location
server may retrieve a previously stored geographical location of
the wireless access point, obtain, through computation according to
signal strength of each wireless access point, a geographical
location corresponding to the terminal, and deliver corresponding
positioning information to the terminal. In this way, the terminal
obtains the positioning information of the location at which the
terminal is currently located. The SLAM technology further means
that in a moving process of a camera, the camera constructs a map
and determines a location of the camera while simultaneously
performing repeated observation. Alternatively, another positioning
method may be used to obtain the positioning information of the
location at which the terminal is currently located, for example,
global positioning system (GPS). This is not limited in this
embodiment of the present disclosure.
[0065] Alternatively, a request for the target scenario to which
the target perception data belongs may be sent to the server.
[0066] Optionally, in this embodiment of the present disclosure,
determining a target scenario to which the target perception data
belongs in step S120 includes sending, to the server, a first
request used to request a scenario to which the target perception
data belongs, and receiving the target scenario that is sent by the
server according to the first request.
[0067] Further, the first request includes the target perception
data, and may further include an identifier of the terminal.
[0068] Step S130: Determine a target perception model corresponding
to the target scenario.
[0069] Further, the target perception model corresponding to the
target scenario may be determined according to a perception model
library locally pre-stored in the terminal. Alternatively, the
target perception model corresponding to the target scenario may be
requested from a network-side server. This is not limited in this
embodiment of the present disclosure.
[0070] Optionally, in this embodiment of the present disclosure,
determining a target perception model corresponding to the target
scenario in step S130 includes determining, from the pre-stored
perception model library, the target perception model corresponding
to the target scenario, where each perception model in the
perception model library corresponds to a scenario.
[0071] Further, the pre-stored perception model library may be
understood as a local storage area that is of the terminal and used
to cache a perception model. Optionally, each perception model may
be stored in the perception model library in a storage form of a
scenario identifier (a scenario number, a scenario, or a type)+a
perception model, that is, each perception model in the perception
model library corresponds to a scenario. For example, when the
target scenario to which the target perception data belongs is a
scenario D, and the perception model library locally cached in the
terminal has a perception model d corresponding to the scenario D,
the perception model d that performs recognition processing on the
target perception data may be directly obtained from the perception
model library.
[0072] It should be understood that, each time the terminal
receives a perception model delivered by the server, the terminal
may cache, in the perception model library for subsequent use, the
received perception model and a scenario identifier corresponding
to the perception model. Optionally, when storage space of the
perception model library is all occupied, an earliest-cached
perception model may be deleted, and then a latest received
perception model is cached in the perception model library.
[0073] The target perception model may be requested from the
network-side server when the perception model library locally
cached in the terminal has no target perception model corresponding
to the target scenario to which the target perception data
belongs.
[0074] Optionally, in this embodiment of the present disclosure,
determining a target perception model corresponding to the target
scenario in step S130 includes sending, to a server, a second
request used to request the perception model corresponding to the
target scenario when the pre-stored perception model library has no
perception model corresponding to the target scenario, where each
perception model in the perception model library corresponds to a
scenario, and receiving the target perception model that
corresponds to the target scenario and sent by the server according
to the second request.
[0075] It should be understood that the second request includes an
identifier indicating the target scenario, and may further include
the identifier of the terminal.
[0076] Optionally, in this embodiment of the present disclosure,
the received target perception model corresponding to the target
scenario may be cached in the perception model library locally
pre-stored in the terminal such that when the target perception
model corresponding to the target scenario needs to be obtained for
a next time, the terminal may directly obtain the target perception
model locally with no need to request the target perception model
from the server again.
[0077] Step S140: Compute a recognition result of the target
perception data according to the target perception model.
[0078] Further, for example, the target perception data is image
data of a camera, and a target scenario to which the target
perception data belongs is a piazza. A corresponding target
perception model, for example, <piazza.model>, is loaded from
the pre-stored perception model library, and the image data of the
camera is analyzed and recognized. A recognition process is as
follows, reading the image, generating multiple partial image areas
in the original image using a sliding window, inputting the partial
area images into a CNN that is configured according to a link
weight parameter in the <piazza.model> file, and outputting
one to multiple recognition results, for example, <flowerbed,
bench, tourist, bus, car, police officer, child, balloon,
cafe>.
[0079] It should be understood that a computation process in step
S140 may be computing a recognition result according to an
algorithm model such as a deep neural network (DNN). For example, a
computation step is as follows, sequentially performing data block
selection, cascade convolution and sampling computation at each
layer of the neural network, and classification matrix computation
on input perception data, to generate a classification result.
[0080] It should be further understood that the computation process
in S140 includes but is not limited to the following, being
completely performed by a general purpose central processing unit
(CPU). For example, if input perception data is image data, the
computation process may be partially performed by a graphics
processing unit (GPU) chip. For another example, if input
perception data is voice data, video data, or the like, the
computation process may be partially performed by a corresponding
dedicated chip. This is not limited in this embodiment of the
present disclosure.
[0081] It should be further understood that, in this embodiment of
the present disclosure, a recognition result of perception data is
obtained through computation using a perception model locally
pre-stored in a terminal, which avoids a problem that a server
needs to respond to a perception data recognition request from each
terminal, and deliver a recognition result of each piece of
perception data one by one. Therefore, according to the data
processing method in this embodiment of the present disclosure, a
computation burden on a network-side server and a bandwidth burden
required in data transmission can be effectively reduced, and a
speed of recognition computation can be further improved.
[0082] Therefore, in the data processing method in this embodiment
of the present disclosure, a scenario to which perception data that
needs to be recognized belongs is determined, and a recognition
result of the perception data is obtained through computation using
a perception model corresponding to the scenario, which can reduce
computation complexity and then can improve data processing
efficiency in comparison with the other approaches.
[0083] Optionally, in this embodiment of the present disclosure,
before obtaining the target perception data, the method further
includes the following steps, which are not shown in FIG. 1.
[0084] Step S150: Update the perception model library according to
a historical scenario sequence of a user, where the updated
perception model library includes the target perception model
corresponding to the target scenario.
[0085] Specific steps are as follows.
[0086] Step S151: Predict, according to the historical scenario
sequence of the user, a scenario to which to-be-recognized
perception data belongs.
[0087] For example, it can be learned according to the perception
model library locally pre-stored in the terminal that scenarios and
time sequences of the scenarios on a workday of a user S are as
follows, 06:00 bedroom, 7:00 living room, 7:20 street, 7:30
highway, and 7:40 garage in a work area. The foregoing scenarios
and time sequences of the scenarios are used as an input sequence
of a conditional random field (CRF) algorithm model, to obtain,
through prediction, a most probable next scenario and a probability
of the scenario, for example, office: 0.83, and conference room:
0.14.
[0088] Step S152: Request, from a server, a perception model
corresponding to the scenario to which the to-be-recognized
perception data belongs.
[0089] For example, a third request used to request perception
models respectively corresponding to an office scenario and a
conference room scenario is sent to the server, and the third
request includes an identifier used to indicate the office scenario
and an identifier indicating the conference room scenario.
[0090] Step S153: Receive the perception model that corresponds to
the scenario to which the to-be-recognized perception data belongs
and sent by the server, and update the locally pre-stored
perception model library.
[0091] Further, the perception models that are respectively
corresponding to the office scenario and the conference room
scenario and sent by the server are received, and the perception
models corresponding to the two scenarios are stored in the locally
pre-stored perception model library in a form of a scenario
identifier (a number or a type)+a perception model. In this case,
when a scenario to which subsequently obtained perception data
belongs is an office, the perception model corresponding to the
office may be directly obtained from the locally pre-stored
perception model library such that a recognition result of the
perception data is obtained according to the updated perception
model.
[0092] Therefore, in this embodiment of the present disclosure, a
recognition result of perception data may be obtained through
computation using a perception model that corresponds to each
scenario and pre-stored in the terminal, which can effectively
improve data processing efficiency.
[0093] The following further describes the present disclosure with
reference to a specific embodiment. It should be understood that
the following embodiment is only intended to help better understand
the present disclosure, instead of limiting the present
disclosure.
[0094] For example, an execution body is a mobile phone, and the
target perception data is a photo taken in the piazza A.
[0095] (1) The mobile phone obtains the photo, and performs
recognition processing on the photo.
[0096] (2) Extract a feature, such as a GIST, dense SIFT, or Texton
Histograms, from the photo, input these features into a previously
trained scenario classification model (an SVM multi-class
classifier), perform scenario recognition, and recognize a scenario
as a piazza.
[0097] (3) Further, obtain positioning information of a location at
which the terminal is currently located.
[0098] (4) Further restrict the scenario of the photo to the piazza
A according to the positioning information.
[0099] (5) Determine whether a perception model library locally
cached in the terminal has a perception model corresponding to the
piazza A, and if the perception model library locally cached in the
terminal has the perception model corresponding to the piazza A, go
to (6), or if the perception model library locally cached in the
terminal has no perception model corresponding to the piazza A, go
to (7).
[0100] (6) Obtain the perception model corresponding to the piazza
A, for example, <piazza.model>, from the perception model
library locally cached in the terminal, and go to (9).
[0101] (7) Send, to a network-side server, an identifier (ID) of
the mobile phone, a request sequence number, and the recognized
scenario, that is, an identifier (a number or a type) of the piazza
A, to request the perception model of the piazza A.
[0102] (8) Receive the perception model that corresponds to the
piazza A and sent by the server, for example, <piazza.model>,
cache the received model parameter file <piazza.model> in the
perception model library in the mobile phone, and delete some
previously cached perception models according to an update policy
if the cache is full.
[0103] (9) Analyze and recognize image data of the photo according
to the perception model <piazza.model>. A specific
recognition process is as follows, reading the image, generating
multiple partial image areas from the original image using a
sliding window, inputting the partial area images into a
convolutional neural network that is configured according to a link
weight parameter in the <piazza.model> file, and outputting
one to multiple recognition results, for example, <flowerbed,
bench, tourist, bus, car, police officer, child, balloon,
cafe>.
[0104] For another example, an execution body is a mobile phone,
and the target perception data is a photo taken in a kitchen of an
indoor user John.
[0105] (1) The mobile phone obtains the photo, and performs
recognition processing on the photo.
[0106] (2) Extract a feature, such as a GIST, dense SIFT, or Texton
Histograms, from the photo, input these features into a previously
trained scenario classification model (an SVM multi-class
classifier), perform scenario recognition, and recognize a scenario
as an indoor scenario.
[0107] (3) Further, obtain an accurate location of the mobile phone
on an indoor map using a WI-FI signal and a SLAM function, that is,
the kitchen of the user John's house.
[0108] (4) Further restrict the scenario of the photo to the
kitchen according to the positioning information.
[0109] (5) Determine whether a perception model library locally
cached in the terminal has a perception model corresponding to the
kitchen, and if the perception model library locally cached in the
terminal has the perception model corresponding to the kitchen, go
to (6), or if the perception model library locally cached in the
terminal has no perception model corresponding to the kitchen, go
to (7).
[0110] (6) Obtain the perception model corresponding to the
kitchen, for example, <kitchen.model>, from the perception
model library locally cached in the terminal, and go to (9).
[0111] (7) Send, to a network-side server, an ID of the mobile
phone, a request sequence number, and the recognized scenario, that
is, an identifier (a number or a type) of the kitchen, to request
the perception model of the kitchen.
[0112] (8) Receive the perception model that corresponds to the
kitchen and sent by the server, for example, <kitchen.
model>, cache the received model parameter file <kitchen.
model> in the perception model library in the mobile phone, and
delete some previously cached perception models according to an
update policy if the cache is full.
[0113] (9) Analyze and recognize image data of the photo according
to the perception model <kitchen. model>. A specific
recognition process is as follows, reading the image, generating
multiple partial image areas from the original image using a
sliding window, inputting the partial area images into a
convolutional neural network that is configured according to a link
weight parameter in the <kitchen. model> file, and outputting
one to multiple recognition results, for example <gas stove,
extractor hood, cupboard, wok, spoon, seasoning box>.
[0114] Therefore, in the data processing method in this embodiment
of the present disclosure, a scenario to which perception data that
needs to be recognized belongs is determined, and a recognition
result of the perception data is obtained through computation using
a perception model corresponding to the scenario, which can reduce
computation complexity and then can improve data processing
efficiency.
[0115] Optionally, in an embodiment, the target perception data may
be directly sent to the server to request the target perception
model used to process the target perception data.
[0116] The processing of the target perception data includes the
following steps.
[0117] (1) Obtain target perception data, where the target
perception data may be any type of the following data, image data,
video data, or voice data.
[0118] (2) Request, from a server, a perception model used to
process the target perception data.
[0119] (3) Receive a target perception model that is sent by the
server after the server determines a target scenario to which the
target perception data belongs and that corresponds to the target
scenario.
[0120] (4) Compute a recognition result of the target perception
data according to the target perception model.
[0121] It should be understood that, in the various embodiments of
the present disclosure, the sequence numbers of the foregoing
processes do not mean an execution order, and the execution order
of the processes should be determined according to functions and
internal logic of the processes, which should not constitute any
limitation on an implementation process of the embodiments of the
present disclosure.
[0122] It should be further understood that, in this embodiment of
the present disclosure, when recognition processing is performed on
target perception data, a target scenario to which the target
perception data belongs is first determined, and then a recognition
result of the target perception data is obtained through
computation using a target perception model corresponding to the
target scenario. Accuracy of recognizing an object in a specific
scenario using a perception model corresponding to the scenario is
relatively high. Therefore, in comparison with a perception
computation model used to process perception data in different
scenarios, computation complexity of a perception model
corresponding to each scenario in this embodiment of the present
disclosure is greatly reduced, thereby effectively lowering a
requirement for a computation capability as well as improving data
processing efficiency. It should be further understood that, the
data processing method in this embodiment of the present disclosure
may be performed by a terminal, or by a server, or by a combination
of a terminal and a server. This is not limited in this embodiment
of the present disclosure.
[0123] Therefore, in the data processing method in this embodiment
of the present disclosure, a scenario to which perception data that
needs to be recognized belongs is determined, and a recognition
result of the perception data is obtained using a perception model
corresponding to the scenario, which can reduce computation
complexity and then can improve data processing efficiency.
[0124] The above describes in detail the data processing method
according to this embodiment of the present disclosure from an
angle of a terminal with reference to FIG. 1. The following
describes a data processing method according to an embodiment of
the present disclosure from an angle of a server with reference to
FIG. 2 and FIG. 3.
[0125] As shown in FIG. 2, a data processing method 200 according
to an embodiment of the present disclosure may be performed, for
example, by a server. The method 200 includes the following
steps.
[0126] Step S210: Receive a request message sent by a terminal and
used to request a perception model corresponding to a scenario to
which target perception data belongs, where the target perception
data is any type of the following data image data, video data, or
voice data.
[0127] Further, the target perception data may be data measured or
generated by a sensing apparatus such as a camera, a microphone, an
infrared sensor, or a depth sensor. For example, the target
perception data is a picture, a video, or a recording.
[0128] The request message may include only the target perception
data, that is, the perception model used to process the target
perception data is directly requested from the server. It should be
understood that, in this case, the server determines the target
scenario to which the target perception data belongs, and further
determines the corresponding target perception model.
Alternatively, the request message may directly include an
identifier used to indicate the target scenario to which the target
perception data belongs, that is, the request message is used to
request the perception model corresponding to the target scenario.
In this case, the server may directly determine the target scenario
according to the request message, and further determine the
corresponding perception model.
[0129] Step S220: Determine, according to the request message, a
target scenario to which the target perception data belongs.
[0130] Further, when the request message sent by the terminal
includes the target perception data, scenario analysis and
recognition may be performed on the target perception data to
determine the target scenario to which the target perception data
belongs. The target scenario may be directly determined according
to the request message when the request message sent by the
terminal indicates the scenario to which the target perception data
belongs. This is not limited in this embodiment of the present
disclosure.
[0131] Optionally, in this embodiment of the present disclosure,
determining, according to the request message, the target scenario
to which the target perception data belongs in step S220 includes
performing scenario analysis on the target perception data included
in the request message, and determining the target scenario to
which the target perception data belongs.
[0132] Further, a scenario recognizer may be used to perform
scenario analysis on the target perception data in order to
determine the target scenario to which the target perception data
belongs. The scenario recognizer may be an existing scenario
classification model, for example, a SVM multi-class classifier.
Further, for example, the target perception data is data obtained
by a camera. A feature such as a GIST, Dense SIFT, or a texton
histogram of an image is extracted from the camera data. These
features are input to a previously trained SVM multi-class
classifier. Scenario recognition is performed, and a scenario type
is output. For example, it is recognized that a scenario is "a
piazza."
[0133] Further, in a case in which the target perception model is
perception data generated at a location at which the terminal is
currently located, the scenario to which the target perception data
belongs may be further restricted according to positioning
information of the location at which the terminal is currently
located.
[0134] Optionally, in this embodiment of the present disclosure,
the target perception data is the data generated at the location at
which the terminal is currently located.
[0135] Determining the target scenario to which the target
perception data belongs includes performing scenario analysis on
the target perception data according to positioning information of
the location at which the terminal is currently located, and
determining the target scenario.
[0136] Further, for example, the target perception data is a photo
taken in a piazza A at which the terminal is currently located. An
SVM multi-class classifier performs scenario analysis on the target
perception data, and a recognition result of the scenario analysis
is that a target scenario to which the target perception data
belongs is a piazza. Then, positioning information of the location
at which the terminal is currently located (that is, the piazza A)
is obtained, and the target scenario to which the target perception
data belongs may be further restricted from the piazza to the
piazza A according to the positioning information. For another
example, the target perception data is a photo taken at a kitchen
at which the terminal is currently located. An SVM multi-class
classifier performs scenario analysis on the target perception
data, and a recognition result of the scenario analysis is that a
target scenario to which the target perception data belongs is an
indoor scenario. Then, positioning information of the location at
which the terminal is currently located (that is, the kitchen) is
obtained, and the target scenario to which the target perception
data belongs may be further restricted from the indoor scenario to
the kitchen according to the positioning information.
[0137] It can be learned that, in this embodiment of the present
disclosure, the scenario to which the target perception data
belongs may be further restricted to a smaller spatial-temporal
area according to the positioning information of the terminal. It
should be understood that a perception model corresponding to a
more specific scenario with a relatively small range is
correspondingly more simplified, computation complexity of the
perception model is relatively low, and a computation workload of
subsequent recognition computation of perception data based on the
perception model is also relatively small.
[0138] It should be understood that, in this embodiment of the
present disclosure, any existing positioning method or a
combination of multiple positioning methods may be used to obtain
positioning information of a current location of a terminal. This
is not limited in the present disclosure.
[0139] The target scenario may be directly determined according to
the request message when the request message sent by the terminal
indicates the scenario to which the target perception data
belongs.
[0140] Optionally, in this embodiment of the present disclosure,
determining, according to the request message, the target scenario
to which the target perception data belongs in step S220 includes
determining the target scenario according to the identifier
included in the request message and used to indicate the target
scenario.
[0141] Step S230: Determine, from a pre-stored perception model
library, a target perception model corresponding to the target
scenario, where each model in the perception model library
corresponds to a scenario.
[0142] The pre-stored perception model library stores perception
models that are respectively corresponding to different scenario
and that are obtained through training according to different
pieces of perception data and sample data of different
scenarios.
[0143] Further, a storage form of the perception models that are of
the different scenarios and in the perception model library may be
a scenario identifier+a perception model. It should be understood
that the storage form of the perception models that are of the
different scenarios and in the perception model library may be any
other forms, which is not limited in this embodiment of the present
disclosure, provided that a corresponding perception model can be
obtained from the perception model library according to a scenario
identifier (a scenario number or a type).
[0144] Step S240: Send the target perception model to the terminal
according to the request message such that the terminal computes a
recognition result of the target perception data according to the
target perception model.
[0145] Therefore, according to the data processing method in this
embodiment of the present disclosure, a perception model
corresponding to a scenario to which perception data that needs to
be recognized belongs is provided to a terminal such that the
terminal processes the corresponding perception data according to
the perception model. Complexity of a perception model
corresponding to a specific scenario is relatively low, and model
accuracy is relatively high. Therefore, computation complexity can
be effectively reduced, and data processing speed and accuracy can
also be improved.
[0146] In this embodiment of the present disclosure, the pre-stored
perception model library stores the perception models that are
respectively corresponding to the different scenarios and obtained
through training according to the different perception data and the
sample data of the different scenarios.
[0147] Optionally, in this embodiment of the present disclosure,
before the request message is received, the method 200 further
includes the following steps, which are not included in FIG. 2.
[0148] Step S250: Obtain a perception data sample, where the
perception data sample includes at least a part of perception data
that has scenario annotation information and object annotation
information.
[0149] Step S260: Train, according to the perception data sample,
perception models respectively corresponding to different
scenarios.
[0150] Step S270: Store, in the perception model library, the
perception models respectively corresponding to the different
scenarios, where the perception model library includes the target
perception model.
[0151] Further, for example, the perception data is image data.
Specific steps of training perception models corresponding to
different scenarios are shown in FIG. 3.
[0152] Step S310: Read an image sample, where at least some images
in the image sample have scenario annotation information and object
annotation information.
[0153] Further, for example, scenario annotation information of an
image Img00001 in the image sample is <scenario: piazza>, and
object annotation information is <objects: flowerbed, bench,
tourist, bus, bus stop, car, police officer, child, balloon, cafe,
pigeon>. Further, the object annotation information of the image
may further include a location of an object in the image, where the
location is represented, for example, using a partial rectangular
area.
[0154] It should be understood that, in this embodiment of the
present disclosure, some images in the image sample may have both
scenario annotation information and object annotation information,
some other images may have only object annotation information, and
still some images may have neither scenario annotation information
nor object annotation information. It should be further understood
that, all images in the image sample may have respective scenario
annotation information and object annotation information. This is
not limited in this embodiment of the present disclosure, provided
that at least some images in the image sample are ensured to have
scenario annotation information and object annotation
information.
[0155] Step S320: Obtain partial area image files of all images
included in the image sample.
[0156] Further, partial area extraction is performed on all the
images included in the image sample that is read. For example,
multiple partial image areas are captured from an image P (any
original image in the image sample) from left to right and from top
to bottom using rectangular sliding windows of different sizes, to
generate multiple partial area image files. In particular, multiple
200*200 and 400.times.400 partial image areas are captured from a
3264.times.2448 original image from left to right and from top to
bottom using rectangular sliding windows whose sizes are
respectively 200.times.200 and 400.times.400, to generate multiple
partial area image files of the original image.
[0157] Step S330: Determine a general perception model according to
the partial area image files and the object annotation information
carried in the image sample, which is also referred to as
determining a parameter file of a general perception model.
[0158] Further, the partial area image files that are of all
original images in the image sample and that are generated in step
S320 are used as input of the general perception model, and the
parameter file of the general perception model is determined with
reference to object type computation information output by the
general perception model and the object annotation information
carried in the image sample.
[0159] It should be understood that the general perception model
may be considered as a model obtained after a CNN model is combined
with a logistic regression (SOFTMAX) model that supports
multi-class classification. For example, steps of determining the
general perception model include using the partial area image files
as input of the CNN model, and correspondingly outputting, by the
CNN model, related matrix information, then, using the matrix
information output by the CNN model as input of the SOFTMAX model,
and correspondingly outputting, by the SOFTMAX model, an object
type computation result, and obtaining parameters of the CNN model
and those of the SOFTMAX model through computation based on the
object type computation result and the object annotation
information (a matching degree between the object type computation
result and the object annotation information, or an error rate)
carried in the image sample, that is, determining the general
perception model.
[0160] It should be further understood that a method for
determining the general perception model in step S330 may be an
existing related method. For brevity, details are not described
herein.
[0161] Step S340: Determine a target image sample, where an image
in the target image sample has scenario annotation information and
object annotation information.
[0162] Further, an image that has both scenario annotation
information and object annotation information is selected from the
image sample that is read in step S310, and images of this type are
determined as the target image sample. For example, annotation
information of first-type images is: <scenario: restaurant;
objects: chair, table, wine bottle, plate, chopsticks>,
annotation information of second-type images is: <scenario:
piazza; objects: flowerbed, bench, tourist, bus, car, police
officer, child, balloon, cafe>, annotation information of
third-type images is: <scenario: piazza; objects: flowerbed,
bench, tourist, bus, bus stop, car, balloon, cafe, pigeon>,
annotation information of fourth-type images is: <scenario:
sickroom; objects: sickbed, patient monitor, respirator, beeper,
support, waste container>, annotation information of fifth-type
images is <scenario: kitchen; objects: kettle, water tap,
microwave oven, salt pot, sugar bowl, tomato sauce, plate, gas
stove>.
[0163] It should be understood that, in the target image sample
determined in step S340, each scenario corresponds to an image set
that includes multiple images, and it should not be understood that
one scenario corresponds to only one image. In other words, the
foregoing mentioned first-type images whose scenario annotation
information is <scenario: restaurant> are a set of multiple
images.
[0164] Step S350: Determine, according to the image in the target
image sample and the object annotation information and the scenario
annotation information of the image and based on the general
perception model, perception models respectively corresponding to
different scenarios.
[0165] Further, partial area image files (obtained in step S320) of
the third-type images whose scenario annotation information is
<scenario: piazza> are used as input of the general
perception model determined in step S330. Correspondingly, the
general perception model outputs object type computation
information obtained through computation. Then, an error rate of
the object type computation information is determined according to
an object type indicated by the object annotation information
carried in the third-type images, and complexity of the general
perception model is measured. Parameters of the general perception
model are adjusted and simplified by considering both the error
rate and the complexity, where simplification of the parameters
includes clustering and combining compute nodes that have similar
parameters, deleting a parameter that does not contribute to
output, and the like. An error rate of the object type computation
information and complexity of the general perception model both
meet a predetermined condition after the parameters of the general
perception model are adjusted and simplified in the foregoing
manner. In this case, the perception model that undergoes parameter
simplification may become a perception model corresponding to the
scenario <piazza>. It should be understood that, in the
foregoing process of determining the perception model corresponding
to the scenario <piazza>, the general perception model
determined in step S330 is backed up for subsequently determining a
perception model corresponding to another scenario. It should be
further understood that, similarly, perception models corresponding
to all other scenarios may be obtained.
[0166] In comparison with globally applicable object recognition, a
quantity of types of objects to be recognized in a specific
scenario is relatively small. Therefore, a quantity of parameters
of a perception model corresponding to each scenario is greatly
reduced in comparison with a quantity of parameters of the general
perception model determined in step S330, which can effectively
reduce computation complexity while improving accuracy of
recognition computation.
[0167] Step S360: Store, in a perception model library, a
perception model corresponding to each scenario.
[0168] It should be understood that the foregoing embodiment is
only intended to help better understand the present disclosure,
instead of limiting the present disclosure.
[0169] It should be understood that types of objects that appear in
each scenario are limited. For example, in an outdoor scenario or a
city scenario, an object such as a person, a vehicle, a building,
or a character may need to be recognized, but basically, there is
no need for recognizing various animals and plants. In other words,
a quantity of types of objects that frequently appear in each
scenario and need to be recognized is relatively small.
Correspondingly, a perception model corresponding to each scenario
has a relatively small quantity of model parameters such that
computation complexity of the perception model corresponding to
each scenario is greatly reduced, and a requirement for storage
space is not very high. Therefore, according to the data processing
method proposed in the present disclosure, complexity and a
computation workload of a perception model can be greatly reduced
while data recognition accuracy is maintained or even improved,
which resolves a contradiction between a computation capability and
model complexity in order to effectively improve a recognition
capability.
[0170] It should be further understood that the foregoing process
of training a perception model corresponding to each scenario
(equivalent to a process of updating a perception model library) is
not limited to be performed for only once before a request message
sent by a terminal and that requests a target perception model for
processing target perception data is received. The process of
training a perception model may also be periodically performed
according to a specific period. In this case, reference may be made
to perception data requested by the terminal in real time, and
perception data and a scenario sample library are continuously
enriched and replenished. Therefore, scenario types and perception
models corresponding to the scenario types are further replenished
and perfected, and accuracy of recognition computation of a
perception model corresponding to each scenario can be continuously
improved.
[0171] Therefore, according to the data processing method in this
embodiment of the present disclosure, a perception model
corresponding to a scenario to which perception data that needs to
be recognized belongs is provided to a terminal such that the
terminal processes the corresponding perception data according to
the perception model. Complexity of a perception model
corresponding to a specific scenario is relatively low, and model
accuracy is relatively high. Therefore, computation complexity can
be effectively reduced, and data processing speed and accuracy can
be improved.
[0172] It should be further understood that, a "pre-stored
perception model library" is mentioned in both the data processing
method 100 and the data processing method 200 in the above. To sum
up, the "pre-stored perception model library" stores a perception
model that corresponds to each scenario and that processes
perception data. However, the "pre-stored perception model library"
in the method 100 is slightly different from that in the method 200
in terms of meaning. Specific descriptions are as follows. The data
processing method 100 provided in the embodiment of the present
disclosure is performed, for example, by a terminal, and the
"pre-stored perception model library" is a storage area, in the
terminal, used to cache a perception model that corresponds to each
scenario and that is obtained from a server. In other words, a
perception model stored in the "pre-stored perception model
library" is a perception model corresponding to a scenario to which
processed or to-be-processed perception data belongs, and may not
include perception models respectively corresponding to all
scenarios. However, the data processing method 200 provided in the
embodiment of the present disclosure is generally performed by a
server, and the pre-stored perception model library is a storage
area, in the server, used to store a perception model that
corresponds to each scenario and that is generated according to
different pieces of perception data and training samples of
different scenarios, which may be understood as that the
"pre-stored perception model library" in the server includes
perception models respectively corresponding to all scenarios.
[0173] The above describes in detail the data processing method
according to the embodiments of the present disclosure with
reference to FIG. 1 to FIG. 3. The following describes in detail a
data processing device according to an embodiment of the present
disclosure with reference to FIG. 4 to FIG. 7.
[0174] FIG. 4 shows a schematic block diagram of a data processing
device 400 according to an embodiment of the present disclosure. As
shown in FIG. 4, the device 400 includes an obtaining module 410
configured to obtain target perception data, where the target
perception data is any type of the following data, image data,
video data, or voice data, a first determining module 420
configured to determine a target scenario to which the target
perception data obtained by the obtaining module 410 belongs, a
second determining module 430 configured to determine a target
perception model corresponding to the target scenario determined by
the first determining module 420, and a computation module 440
configured to compute, according to the target perception model
determined by the second determining module 430, a recognition
result of the target perception data obtained by the obtaining
module 410.
[0175] Therefore, the data processing device 400 in this embodiment
of the present disclosure determines a scenario to which perception
data that needs to be recognized belongs, and obtains a recognition
result of the perception data through computation using a
perception model corresponding to the scenario, which can reduce
computation complexity and then can improve data processing
efficiency in comparison with the other approaches.
[0176] Further, the computation module 440 may be a perception
computation processor, and a function of the computation model 440
is to perform perception computation, for example, performing
recognition processing on perception data according to an algorithm
model such as a CNN or a DNN. Data block selection, cascade
convolution and sampling computation at each layer of the neural
network, and classification matrix computation are sequentially
performed on input perception data, to generate a recognition
result. A computation process includes but is not limited to the
following, being completely performed by a general purpose CPU, or
partially performed by a GPU acceleration chip, or performed by a
dedicated chip.
[0177] Optionally, in an embodiment, the first determining module
420 is further configured to perform scenario analysis on the
target perception data, and determine the target scenario.
[0178] Further, the first determining module 420 may be a scenario
recognizer, and a function of the first determining module 420 is
to recognize a scenario to which input perception data belongs. The
perception data is input, and a scenario type or scenario number or
another identifier that may represent a scenario is output.
[0179] Optionally, in an embodiment, the target perception data is
data generated at a location at which a terminal is currently
located.
[0180] The first determining module 420 is further configured to
perform scenario analysis on the target perception data according
to positioning information of the location at which the terminal is
currently located, and determine the target scenario.
[0181] Further, the perception data and the positioning information
are input into the first determining module 420, and a scenario
type or number is output.
[0182] Optionally, in an embodiment, the first determining module
420 includes a first sending unit (not shown) configured to send,
to a server, a first request used to request a scenario to which
the target perception data belongs, and a first receiving unit (not
shown) configured to receive the target scenario that is sent by
the server according to the first request.
[0183] Optionally, in an embodiment, the second determining module
430 is further configured to determine, from a pre-stored
perception model library, the target perception model corresponding
to the target scenario, where each perception model in the
perception model library corresponds to a scenario.
[0184] Optionally, as shown in FIG. 5, in an embodiment, a data
processing device 400 includes an obtaining module 410 configured
to obtain target perception data, where the target perception data
is any type of the following data image data, video data, or voice
data, a first determining module 420 configured to determine a
target scenario to which the target perception data obtained by the
obtaining module 410 belongs, a second determining module 430
configured to determine a target perception model corresponding to
the target scenario determined by the first determining module 420,
a computation module 440 configured to compute, according to the
target perception model determined by the second determining module
430, a recognition result of the target perception data obtained by
the obtaining module 410, and an updating module 450 configured to
update a perception model library according to a historical
scenario sequence of a user before the obtaining module 410 obtains
the target perception data, where the updated perception model
library includes the target perception model corresponding to the
target scenario.
[0185] It should be understood that the updating module 450 may
store the target perception model in the pre-stored perception
model library after the second determining module 430 obtains the
target perception model by requesting the target perception model
from a server in order to update the perception model library. The
updating module 450 may further request, before the obtaining
module 410 obtains the target perception data on which recognition
computation needs to be performed, a to-be-needed perception model
from a server in advance using a prediction algorithm, that is,
update the pre-stored perception model library in advance.
[0186] Optionally, in an embodiment, the second determining module
430 includes a second sending unit (not shown) configured to send,
to a server, a second request used to request the perception model
corresponding to the target scenario when the pre-stored perception
model library has no perception model corresponding to the target
scenario, where each perception model in the perception model
library corresponds to a scenario, and a second receiving unit (not
shown) configured to receive the target perception model that
corresponds to the target scenario and sent by the server according
to the second request.
[0187] Optionally, in this embodiment of the present disclosure,
the data processing device 400 further includes a caching module
(not shown) configured to cache, in the pre-stored perception model
library, the target perception model received by the second
receiving unit and a scenario identifier (a scenario type or a
scenario number). Further, the caching module may be a high-speed
access device such as a memory.
[0188] Therefore, the data processing device 400 in this embodiment
of the present disclosure determines a scenario to which perception
data that needs to be recognized belongs, and obtains a recognition
result of the perception data through computation using a
perception model corresponding to the scenario, which can reduce
computation complexity and then can improve data processing
efficiency in comparison with the other approaches.
[0189] It should be understood that, the data processing device 400
according to this embodiment of the present disclosure may be
corresponding to the terminal in the data processing method in the
embodiments of the present disclosure. In addition, the foregoing
and other operations and/or functions of the modules in the device
400 are respectively used to implement corresponding processes of
the methods in FIG. 1 to FIG. 3. For brevity, details are not
described herein again.
[0190] The above describes in detail the data processing device 400
according to the embodiments of the present disclosure with
reference to FIG. 4 and FIG. 5. The following describes in detail
another data processing device according to an embodiment of the
present disclosure with reference to FIG. 6 and FIG. 7.
[0191] FIG. 6 shows a schematic block diagram of a data processing
device 500 according to an embodiment of the present disclosure. As
shown in FIG. 6, the data processing device 500 includes a
receiving module 510 configured to receive a request message that
is sent by a terminal and used to request a perception model
corresponding to a scenario to which target perception data
belongs, where the target perception data is any type of the
following data, image data, video data, or voice data, a first
determining module 520 configured to determine, according to the
request message received by the receiving module 510, a target
scenario to which the target perception data belongs, a second
determining module 530 configured to determine, from a pre-stored
perception model library, the target perception model corresponding
to the target scenario determined by the first determining module
520, where each model in the perception model library corresponds
to a scenario, and a sending module 540 configured to send, to the
terminal according to the request message received by the receiving
module 510, the target perception model determined by the second
determining module 530 such that the terminal computes a
recognition result of the target perception data according to the
target perception model.
[0192] Therefore, the data processing device 500 in this embodiment
of the present disclosure provides, to a terminal, a perception
model that corresponds to a scenario and needed by the terminal
such that the terminal processes corresponding perception data
according to the perception model. Complexity of a perception model
corresponding to a specific scenario is relatively low, and model
accuracy is relatively high. Therefore, computation complexity can
be effectively reduced, and data processing speed and accuracy can
be improved.
[0193] As shown in FIG. 7, optionally, in an embodiment, the device
500 includes a receiving module 510 configured to receive a request
message that is sent by a terminal and used to request a perception
model corresponding to a scenario to which target perception data
belongs, where the target perception data is any type of the
following data, image data, video data, or voice data, a first
determining module 520 configured to determine, according to the
request message received by the receiving module 510, a target
scenario to which the target perception data belongs, a second
determining module 530 configured to determine, from a pre-stored
perception model library, the target perception model corresponding
to the target scenario determined by the first determining module
520, where each model in the perception model library corresponds
to a scenario, a sending module 540 configured to send, to the
terminal according to the request message received by the receiving
module 510, the target perception model determined by the second
determining module 530 such that the terminal computes a
recognition result of the target perception data according to the
target perception model, an obtaining module 550 configured to
obtain a perception data sample before the receiving module 510
receives the request message, where the perception data sample
includes at least a part of perception data that has scenario
annotation information and object annotation information, a
training module 560 configured to train, according to the
perception data sample, perception models respectively
corresponding to different scenarios, and a storage module 570
configured to store, in the perception model library, the
perception models that are respectively corresponding to the
different scenarios and obtained through training by the training
module 560, where the perception model library includes the target
perception model.
[0194] Further, the training module 560 may be referred to as a
model training server. A function of the model training server is
to read a training sample database, and train, according to each
scenario classification description in a scenario knowledge base,
perception model parameters needed in each scenario. The training
sample data and a scenario classification description file are
input into the training module 560, and a perception model
parameter file of each scenario is output.
[0195] The scenario knowledge base is storage space used to manage
and store classification descriptions corresponding to various
scenarios. The classification descriptions corresponding to various
scenarios include categories that may occur in various scenarios,
for example, an object, a person, an action behavior, an event, and
a character, and may further include a hierarchical relationship
between all the categories, for example, animal-dog-Golden
Retriever, vehicle-car-BMW-BMW 3 series, and party-birthday party.
In addition, for a specific scenario with a known space structure,
the scenario knowledge base may further include space structure
information and a scenario number corresponding to each space
area.
[0196] Further, the storage module 570 is configured to store the
model parameter file that is of each scenario and generated by the
training module 560 (the model training server). For example, the
model parameter file includes a scenario identifier (a type or a
number) and a corresponding model parameter file. The storage
module 570 may be referred to as a model parameter library.
[0197] Optionally, in an embodiment, the first determining module
520 is further configured to perform scenario analysis on the
target perception data included in the request message, and
determine the target scenario to which the target perception data
belongs.
[0198] Optionally, in an embodiment, the target perception data is
data generated at a location at which the terminal is currently
located.
[0199] The first determining module 520 is further configured to
perform scenario analysis on the target perception data according
to positioning information of the location at which the terminal is
currently located, and determine the target scenario.
[0200] Optionally, in an embodiment, the first determining module
520 is further configured to determine the target scenario
according to an identifier that is included in the request message
and used to indicate the target scenario.
[0201] Therefore, the data processing device 500 in this embodiment
of the present disclosure provides, to a terminal, a perception
model that corresponds to a scenario and needed by the terminal
such that the terminal processes corresponding perception data
according to the perception model. Complexity of a perception model
corresponding to a specific scenario is relatively low, and model
accuracy is relatively high. Therefore, computation complexity can
be effectively reduced, and data processing speed and accuracy can
be improved.
[0202] It should be understood that, the data processing device 500
according to this embodiment of the present disclosure may be
corresponding to the server in the data processing method in the
embodiments of the present disclosure. In addition, the foregoing
and other operations and/or functions of the modules in the device
500 are respectively used to implement corresponding processes of
the methods in FIG. 1 to FIG. 3. For brevity, details are not
described herein again.
[0203] As shown in FIG. 8, an embodiment of the present disclosure
further provides a data processing device 600. The data processing
device 600 includes a processor 610, a memory 620, a bus system
630, a receiver 640, and a sender 650. The processor 610, the
memory 620, the receiver 640, and the sender 650 are connected
using the bus system 630. The memory 620 is configured to store an
instruction. The processor 610 is configured to execute the
instruction stored in the memory 620 in order to control the
receiver 640 to receive a signal and control the sender 650 to send
a signal. The processor 610 is configured to obtain target
perception data, where the target perception data is any type of
the following data, image data, video data, or voice data,
determine a target scenario to which the target perception data
belongs, determine a target perception model corresponding to the
target scenario, and compute a recognition result of the target
perception data according to the target perception model.
[0204] Therefore, the data processing device 600 in this embodiment
of the present disclosure determines a scenario to which perception
data that needs to be recognized belongs, and obtains a recognition
result of the perception data through computation using a
perception model corresponding to the scenario, which can reduce
computation complexity and then can improve data processing
efficiency in comparison with the other approaches.
[0205] Optionally, in an embodiment, the processor 610 is further
configured to perform scenario analysis on the target perception
data, and determine the target scenario.
[0206] Optionally, in an embodiment, the target perception data is
data generated at a location at which a terminal is currently
located.
[0207] The processor 610 is further configured to perform scenario
analysis on the target perception data according to positioning
information of the location at which the terminal is currently
located, and determine the target scenario.
[0208] Optionally, in an embodiment, the sender 650 is configured
to send, to a server, a first request used to request the scenario
to which the target perception data belongs. The receiver 640 is
configured to receive the target scenario sent by the server
according to the first request.
[0209] Optionally, in an embodiment, the processor 610 is further
configured to determine, from a pre-stored perception model
library, the target perception model corresponding to the target
scenario, where each perception model in the perception model
library corresponds to a scenario.
[0210] Optionally, in an embodiment, the processor 610 is further
configured to update the perception model library according to a
historical scenario sequence of a user before obtaining the target
perception data, where the updated perception model library
includes the target perception model corresponding to the target
scenario.
[0211] Optionally, in an embodiment, the sender 650 is configured
to send, to a server, a second request used to request the
perception model corresponding to the target scenario when a
pre-stored perception model library has no perception model
corresponding to the target scenario, where each perception model
in the perception model library corresponds to a scenario. The
receiver 640 is configured to receive the target perception model
that corresponds to the target scenario and sent by the server
according to the second request.
[0212] It should be understood that, in this embodiment of the
present disclosure, the processor 610 may be a CPU, or the
processor 610 may be another general purpose processor, a digital
signal processor (DSP), an application-specific integrated circuit
(ASIC), a field programmable gate array (FPGA) or another
programmable logic device, a discrete gate or a transistor logic
device, a discrete hardware component, or the like. The general
purpose processor may be a microprocessor, or the processor may be
any regular processor.
[0213] The memory 620 may include a read-only memory (ROM) and a
random access memory (RAM), and provides an instruction and data to
the processor 610. A part of the memory 620 may further include a
non-volatile RAM. For example, the memory 620 may further store
device type information.
[0214] In addition to a data bus, the bus system 630 may further
include a power bus, a control bus, a status signal bus, and the
like. However, for clear description, various buses are all marked
as the bus system 630 in the figure.
[0215] In an implementation process, the steps of the foregoing
method may be completed using an integrated logic circuit of
hardware in the processor 610 or using an instruction in a software
form. The steps of the method disclosed with reference to the
embodiments of the present disclosure may be directly performed and
completed by a hardware processor, or performed and completed by a
combination of hardware and software modules in the processor. The
software module may be located in a mature storage medium in the
art, such as a RAM, a flash memory, a ROM, a programmable read-only
memory (PROM) or an electrically erasable programmable read-only
memory (EEPROM), or a register. The storage medium is located in
the memory 620, and the processor 610 reads information from the
memory 620 and completes the steps of the foregoing method in
combination with hardware of the processor 610. To avoid
repetition, details are not described herein again.
[0216] Therefore, the data processing device 600 in this embodiment
of the present disclosure determines a scenario to which perception
data that needs to be recognized belongs, and obtains a recognition
result of the perception data through computation using a
perception model corresponding to the scenario, which can reduce
computation complexity and then can improve data processing
efficiency in comparison with the other approaches.
[0217] It should be understood that, the data processing device 600
according to this embodiment of the present disclosure may be
corresponding to the terminal in the data processing method in the
embodiments of the present disclosure, and the device 600 may be
further corresponding to the data processing device 400 in the
embodiments of the present disclosure. In addition, the foregoing
and other operations and/or functions of the modules in the device
600 are respectively used to implement corresponding processes of
the methods in FIG. 1 to FIG. 3. For brevity, details are not
described herein again.
[0218] As shown in FIG. 9, an embodiment of the present disclosure
further provides a data processing device 700. The device 700
includes a processor 710, a memory 720, a bus system 730, a
receiver 740, and a sender 750. The processor 710, the memory 720,
the receiver 740, and the sender 750 are connected using the bus
system 730. The memory 720 is configured to store an instruction.
The processor 710 is configured to execute the instruction stored
in the memory 720 in order to control the receiver 740 to receive a
signal and control the sender 750 to send a signal.
[0219] The receiver 740 is configured to receive a request message
that is sent by a terminal and used to request a perception model
corresponding to a scenario to which target perception data
belongs, where the target perception data is any type of the
following data, image data, video data, or voice data. The
processor 710 is configured to determine, according to the request
message, the target scenario to which the target perception data
belongs, and determine, from a pre-stored perception model library,
the target perception model corresponding to the target scenario,
where each model in the perception model library corresponds to a
scenario. The sender 750 is configured to send the target
perception model to the terminal according to the request message
such that the terminal computes a recognition result of the target
perception data according to the target perception model.
[0220] Therefore, the data processing device 700 in this embodiment
of the present disclosure provides, to a terminal, a perception
model that corresponds to a scenario and needed by the terminal
such that the terminal processes corresponding perception data
according to the perception model. Complexity of a perception model
corresponding to a specific scenario is relatively low, and model
accuracy is relatively high. Therefore, computation complexity can
be effectively reduced, and data processing speed and accuracy can
be improved.
[0221] Optionally, in an embodiment, the processor 710 is further
configured to obtain a perception data sample before the receiver
740 receives the request message, where the perception data sample
includes at least a part of perception data that has scenario
annotation information and object annotation information, train,
according to the perception data sample, perception models
respectively corresponding to different scenarios, and store, in
the perception model library, the perception models respectively
corresponding to the different scenarios, where the perception
model library includes the target perception model.
[0222] Optionally, in an embodiment, the processor 710 is further
configured to perform scenario analysis on the target perception
data in the request message, and determine the target scenario to
which the target perception data belongs.
[0223] Optionally, in an embodiment, the target perception data is
data generated at a location at which the terminal is currently
located.
[0224] The processor 710 is further configured to perform scenario
analysis on the target perception data according to positioning
information of the location at which the terminal is currently
located, and determine the target scenario.
[0225] Optionally, in an embodiment, the processor 710 is further
configured to determine the target scenario according to an
identifier that is included in the request message and used to
indicate the target scenario.
[0226] It should be understood that, in this embodiment of the
present disclosure, the processor 710 may be a CPU, or the
processor 710 may be another general purpose processor, a DSP, an
ASIC, an FPGA or another programmable logic device, a discrete gate
or a transistor logic device, a discrete hardware component, or the
like. The general purpose processor may be a microprocessor, or the
processor 710 may be any regular processor.
[0227] The memory 720 may include a ROM and a RAM, and provides an
instruction and data to the processor 710. A part of the memory 720
may further include a non-volatile RAM. For example, the memory 720
may further store device type information.
[0228] In addition to a data bus, the bus system 730 may further
include a power bus, a control bus, a status signal bus, and the
like. However, for clear description, various buses are all marked
as the bus system 730 in the figure.
[0229] In an implementation process, the steps of the foregoing
method may be completed using an integrated logic circuit of
hardware in the processor 710 or using an instruction in a software
form. The steps of the method disclosed with reference to the
embodiments of the present disclosure may be directly performed and
completed by a hardware processor, or performed and completed by a
combination of hardware and software modules in the processor 710.
The software module may be located in a mature storage medium in
the art, such as a RAM, a flash memory, a ROM, a PROM or an EEPROM,
or a register. The storage medium is located in the memory 720, and
the processor 710 reads information from the memory 720 and
completes the steps of the foregoing method in combination with
hardware of the processor 720. To avoid repetition, details are not
described herein again.
[0230] Therefore, the data processing device 700 in this embodiment
of the present disclosure provides, to a terminal, a perception
model that corresponds to a scenario and needed by the terminal
such that the terminal processes corresponding perception data
according to the perception model. Complexity of a perception model
corresponding to a specific scenario is relatively low, and model
accuracy is relatively high. Therefore, computation complexity can
be effectively reduced, and data processing speed and accuracy can
be improved.
[0231] It should be understood that, the data processing device 700
according to this embodiment of the present disclosure may be
corresponding to the server in the data processing method in the
embodiments of the present disclosure, and the device 700 may be
further corresponding to the data processing device 500 in the
embodiments of the present disclosure. In addition, the foregoing
and other operations and/or functions of the modules in the device
700 are respectively used to implement corresponding processes of
the methods in FIG. 1 to FIG. 3. For brevity, details are not
described herein again.
[0232] A person of ordinary skill in the art may be aware that, in
combination with the examples described in the embodiments
disclosed in this specification, units and algorithm steps may be
implemented by electronic hardware or a combination of computer
software and electronic hardware. Whether the functions are
performed in a hardware or software manner depends on particular
applications and design constraint conditions of the technical
solutions. A person skilled in the art may use different methods to
implement the described functions for each particular application,
but it should not be considered that the implementation goes beyond
the scope of the present disclosure.
[0233] It may be clearly understood by a person skilled in the art
that, for the purpose of convenient and brief description, for a
specific working process of a system, an apparatus, and a unit that
are described above, reference may be made to a corresponding
process in the foregoing method embodiments, and details are not
described herein again.
[0234] In the several embodiments provided in this application, it
should be understood that the disclosed system, apparatus, and
method may be implemented in other manners. For example, the
described apparatus embodiment is merely exemplary. For example,
the unit division is merely logical function division and may be
other division in actual implementation. For example, a plurality
of units or components may be combined or integrated into another
system, or some features may be ignored or not performed. In
addition, the displayed or discussed mutual couplings or direct
couplings or communication connections may be implemented using
some interfaces. The indirect couplings or communication
connections between the apparatuses or units may be implemented in
electronic, mechanical, or other forms.
[0235] The units described as separate parts may or may not be
physically separate, and parts displayed as units may or may not be
physical units, may be located in one position, or may be
distributed on a plurality of network units. Some or all of the
units may be selected according to actual needs to achieve the
objectives of the solutions of the embodiments.
[0236] In addition, functional units in the embodiments of the
present disclosure may be integrated into one processing unit, or
each of the units may exist alone physically, or two or more units
are integrated into one unit.
[0237] When the functions are implemented in the form of a software
functional unit and sold or used as an independent product, the
functions may be stored in a computer-readable storage medium.
Based on such an understanding, the technical solutions of the
present disclosure, or some of the technical solutions, may be
implemented in a form of a software product. The computer software
product is stored in a storage medium, and includes some
instructions for instructing a computer device (which may be a
personal computer, a server, a network device, or the like) to
perform all or some of the steps of the methods described in the
embodiments of the present disclosure. The foregoing storage medium
includes any medium that can store program code, such as a
universal serial bus (USB) flash drive, a removable hard disk, a
ROM, a RAM, a magnetic disk, or an optical disc.
[0238] The foregoing descriptions are merely specific
implementation manners of the present disclosure, but are not
intended to limit the protection scope of the present disclosure.
Any variation or replacement readily figured out by a person
skilled in the art within the technical scope disclosed in the
present disclosure shall fall within the protection scope of the
present disclosure. Therefore, the protection scope of the present
disclosure shall be subject to the protection scope of the
claims.
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