U.S. patent application number 16/258863 was filed with the patent office on 2019-05-23 for analysis apparatus, analysis method, and storage medium.
This patent application is currently assigned to NEC Corporation. The applicant listed for this patent is NEC Corporation. Invention is credited to Takuya ARAKI, Yasufumi HIRAKAWA, Jianquan LIU, Shoji NISHIMURA.
Application Number | 20190156508 16/258863 |
Document ID | / |
Family ID | 59962929 |
Filed Date | 2019-05-23 |
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United States Patent
Application |
20190156508 |
Kind Code |
A1 |
HIRAKAWA; Yasufumi ; et
al. |
May 23, 2019 |
ANALYSIS APPARATUS, ANALYSIS METHOD, AND STORAGE MEDIUM
Abstract
Provided is an analysis apparatus (10) including a person
extraction unit (11) that analyzes video data to extract a person,
a time calculation unit (12) that calculates a continuous
appearance time period for which the extracted person has been
continuously present in a predetermined area and a reappearance
time interval until the extracted person reappears in the
predetermined area for each extracted person, and an inference unit
(13) that infers a characteristic of the extracted person on the
basis of the continuous appearance time period and the reappearance
time interval.
Inventors: |
HIRAKAWA; Yasufumi; (Tokyo,
JP) ; LIU; Jianquan; (Tokyo, JP) ; NISHIMURA;
Shoji; (Tokyo, JP) ; ARAKI; Takuya; (Tokyo,
JP) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
NEC Corporation |
Tokyo |
|
JP |
|
|
Assignee: |
NEC Corporation
Tokyo
JP
|
Family ID: |
59962929 |
Appl. No.: |
16/258863 |
Filed: |
January 28, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16080745 |
Aug 29, 2018 |
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PCT/JP2017/005100 |
Feb 13, 2017 |
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16258863 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04N 5/232 20130101;
G06T 7/20 20130101; G06K 9/00771 20130101; G06K 9/00744 20130101;
G08B 25/00 20130101; G06T 2207/30232 20130101; G06T 7/70 20170101;
H04N 7/18 20130101; G06K 9/00228 20130101 |
International
Class: |
G06T 7/70 20060101
G06T007/70; H04N 7/18 20060101 H04N007/18; G06K 9/00 20060101
G06K009/00; G08B 25/00 20060101 G08B025/00; H04N 5/232 20060101
H04N005/232; G06T 7/20 20060101 G06T007/20 |
Foreign Application Data
Date |
Code |
Application Number |
Mar 30, 2016 |
JP |
2016-067538 |
Claims
1. An analysis apparatus comprising a memory storing instructions;
and at least one processor executing the instructions to perform:
extracting a person from a video data; calculating a time period
for which the extracted person has been continuously present in a
predetermined area and a time interval between first point in time
when the extracted person disappears from the predetermined area
and second point in time when the extracted person reappears to the
predetermined area; inferring one or more personal characteristics
that may be corresponding to the extracted person based on the
calculated time period and the calculated time interval;
calculating probability that the person is corresponding to each
personal characteristic; and outputting information based on the
probability.
2. The analysis apparatus according to claim 1, wherein the at
least one processor executes the instructions to perform: when an
input specifying a personal characteristic is received, displaying
a list of persons corresponding to the specified personal
characteristic.
3. The analysis apparatus according to claim 1, wherein the at
least one processor executes the instructions to perform: when an
input specifying a personal characteristic and a period is
received, displaying a list of persons corresponding to the
specified personal characteristic within the period.
4. An analysis method performed by a computer, the method
comprising: extracting a person from a video data; calculating a
time period for which the extracted person has been continuously
present in a predetermined area and a time interval between first
point in time when the extracted person disappears from the
predetermined area and second point in time when the extracted
person reappears to the predetermined area; inferring one or more
personal characteristics that may be corresponding to the extracted
person based on the calculated time period and the calculated time
interval; calculating probability that the person is corresponding
to each personal characteristic; and outputting information based
on the probability.
5. The analysis method according to claim 4, wherein when an input
specifying a personal characteristic is received, displaying a list
of persons corresponding to the specified personal
characteristic.
6. The analysis method according to claim 4, wherein when an input
specifying a personal characteristic and a period is received,
displaying a list of persons corresponding to the specified
personal characteristic within the period.
7. A non-transitory computer readable recording medium storing
programs, the programs causing a computer to perform: extracting a
person from a video data; calculating a time period for which the
extracted person has been continuously present in a predetermined
area and a time interval between first point in time when the
extracted person disappears from the predetermined area and second
point in time when the extracted person reappears to the
predetermined area; inferring one or more personal characteristics
that may be corresponding to the extracted person based on the
calculated time period and the calculated time interval;
calculating probability that the person is corresponding to each
personal characteristic; and outputting information based on the
probability.
8. The non-transitory computer readable recording medium according
to claim 7, wherein the programs causes the computer to perform:
when an input specifying a personal characteristic is received,
displaying a list of persons corresponding to the specified
personal characteristic.
9. The non-transitory computer readable recording medium according
to claim 7, wherein the programs causes the computer to perform:
when an input specifying a personal characteristic and a period is
received, displaying a list of persons corresponding to the
specified personal characteristic within the period.
Description
[0001] The present application is a Continuation application of
U.S. patent application Ser. No. 16/080,745 filed on Aug. 29, 2018,
which is a National Stage Entry of international application
PCT/JP2017/005100, filed Feb. 13, 2017, which claims the benefit of
priority from Japanese Patent Application 2016-067538 filed on Mar.
30, 2016, the disclosures of all of which are incorporated herein,
in their entirety, by this reference.
TECHNICAL FIELD
[0002] The invention relates to an analysis apparatus, an analysis
method, and a program.
BACKGROUND ART
[0003] The related art is disclosed in Patent Document 1. The
Patent Document 1 discloses a countermeasure system against
suspicious persons which detects the face of a person in a captured
image of a scene in a surveillance range and determines whether
countermeasures are needed or the degree of countermeasures on the
basis of, for example, the size of the face, the time for which the
person has been continuously present in the surveillance range, or
the number of times the person appears in the surveillance range.
It is assumed that, as the length of time or the number of times
described above increases, the possibility that the person is a
suspicious person increases.
[0004] Patent Documents 2 and 3 disclose an index generation
apparatus that generates an index in which a plurality of nodes are
hierarchized.
RELATED DOCUMENT
Patent Document
[0005] [Patent Document 1] Japanese Patent Application Publication
No. 2006-11728
[0006] [Patent Document 2] WO2014/109127
[0007] [Patent Document 3] Japanese Patent Application Publication
No. 2015-49574
SUMMARY OF THE INVENTION
Technical Problem
[0008] In a case in which, as the time period for which a person
has been continuously present or the number of times the person
appears increases, the possibility that the person is a suspicious
person is determined to be higher as in the technique disclosed in
the Patent Document 1, a determination error is likely to occur.
For example, a person who continuously works in the surveillance
range is determined to be a suspicious person. In order to solve
this problem, it is desirable to have various criteria for
determination.
[0009] An object of the invention is to provide a new technique for
inferring a characteristic of a person extracted from an image.
Solution to Problem
[0010] In one exemplary embodiment of the invention, there is
provided an analysis apparatus comprising: a person extraction unit
that analyzes video data to extract a person; a time calculation
unit that calculates a continuous appearance time period for which
the extracted person has been continuously present in a
predetermined area and a reappearance time interval until the
extracted person reappears in the predetermined area for each
extracted person; and an inference unit that infers a
characteristic of the extracted person on the basis of the
continuous appearance time period and the reappearance time
interval.
[0011] In another exemplary embodiment of the invention, there is
provided an analysis method performed by a computer, the method
comprising: a person extraction step of analyzing video data to
extract a person; a time calculation step of calculating a
continuous appearance time period for which the extracted person
has been continuously present in a predetermined area and a
reappearance time interval until the extracted person reappears in
the predetermined area for each extracted person; and an inference
step of inferring a characteristic of the extracted person on the
basis of the continuous appearance time period and the reappearance
time interval.
[0012] In still another exemplary embodiment of the invention,
there is provided a program that causes a computer to function as:
a person extraction unit that analyzes video data to extract a
person; a time calculation unit that calculates a continuous
appearance time period for which the extracted person has been
continuously present in a predetermined area and a reappearance
time interval until the extracted person reappears in the
predetermined area for each extracted person; and an inference unit
that infers a characteristic of the extracted person on the basis
of the continuous appearance time period and the reappearance time
interval.
Advantageous Effects of Invention
[0013] According to the invention, it is possible to provide a new
technique for inferring the characteristic of a person extracted
from an image.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The above objects and other objects, features and advantages
will become more apparent from the following description of the
preferred example embodiments and the accompanying drawings
below.
[0015] FIG. 1 is a conceptual diagram illustrating an example of
the hardware configuration of an apparatus according to an
exemplary embodiment.
[0016] FIG. 2 is an example of a functional block diagram of an
analysis apparatus according to the exemplary embodiment.
[0017] FIG. 3 is a diagram illustrating an example of a method of
calculating a continuous appearance time period and a reappearance
time interval according to the exemplary embodiment.
[0018] FIG. 4 is a diagram illustrating an example of the method of
calculating the continuous appearance time period and the
reappearance time interval according to the exemplary
embodiment.
[0019] FIG. 5 is a diagram illustrating an example of index
information that may be used in the exemplary embodiment.
[0020] FIG. 6 is a diagram schematically illustrating an example of
information handled by the analysis apparatus according to the
exemplary embodiment.
[0021] FIG. 7 is a diagram schematically illustrating an example of
the information handled by the analysis apparatus according to the
exemplary embodiment.
[0022] FIG. 8 is a diagram schematically illustrating an example of
the information handled by the analysis apparatus according to the
exemplary embodiment.
[0023] FIG. 9 is a diagram schematically illustrating an example of
the information handled by the analysis apparatus according to the
exemplary embodiment.
[0024] FIG. 10 is a diagram schematically illustrating an example
of the information handled by the analysis apparatus according to
the exemplary embodiment.
[0025] FIG. 11 is a flowchart illustrating an example of the flow
of a process of the analysis apparatus according to the exemplary
embodiment.
[0026] FIG. 12 is an example of the functional block diagram
illustrating the analysis apparatus according to the exemplary
embodiment.
[0027] FIG. 13 is a diagram schematically illustrating an example
of the information handled by the analysis apparatus according to
the exemplary embodiment.
[0028] FIG. 14 is an example of the functional block diagram
illustrating the analysis apparatus according to the exemplary
embodiment.
[0029] FIG. 15 is a diagram illustrating an example of a setting
screen provided by the analysis apparatus according to the
exemplary embodiment.
[0030] FIG. 16 is a diagram illustrating an example of the setting
screen provided by the analysis apparatus according to the
exemplary embodiment.
DESCRIPTION OF EMBODIMENTS
[0031] First, an example of the hardware configuration of an
apparatus (analysis apparatus) according to an exemplary embodiment
will be described. Each unit of the apparatus according to the
exemplary embodiment is implemented by an arbitrary combination of
software and hardware including a central processing unit (CPU), a
memory, a program loaded to the memory, a storage unit (which can
store a program loaded from a storage medium, such as a compact
disc (CD), or a server on the Internet, in addition to a program
that is stored in the apparatus in advance in a shipment stage),
such as a hard disk storing the program, and an interface for
connection to a network of an arbitrary computer. It will be
understood by those skilled in the art that there are various
modification examples of the implementation method and the
apparatus.
[0032] FIG. 1 is a block diagram illustrating the hardware
configuration of the apparatus according to the exemplary
embodiment. As illustrated in FIG. 1, the apparatus includes a
processor 1A, a memory 2A, an input/output interface 3A, a
peripheral circuit 4A, and a bus 5A. The peripheral circuit
includes various modules.
[0033] The bus 5A is a data transmission path through which the
processor 1A, the memory 2A, the peripheral circuit 4A, and the
input/output interface 3A transmit and receive data. The processor
1A is an arithmetic processing unit such as a central processing
unit (CPU) or a graphics processing unit (GPU). The memory 2A is,
for example, a random access memory (RAM) or a read only memory
(ROM). The input/output interface 3A includes, for example, an
interface for acquiring information from an external apparatus, an
external server, and an external sensor. The processor 1A outputs
commands to each module and performs calculation on the basis of
the calculation results of the modules.
[0034] Next, the exemplary embodiment will be described. The
functional block diagrams used in the following description of the
exemplary embodiment do not illustrate the structure of each
hardware unit, but illustrate a functional unit block. In the
diagrams, each apparatus is implemented by one device. However, a
means of implementing each apparatus is not limited thereto. That
is, each apparatus may be physically divided or may be logically
divided. The same components are denoted by the same reference
numerals and the description thereof will not be repeated.
First Exemplary Embodiment
[0035] First, the outline of this exemplary embodiment will be
described. An analysis apparatus according to this exemplary
embodiment analyzes video data to extract a person. Then, the
analysis apparatus calculates the time period (continuous
appearance time period) for which the extracted person has been
continuously present in a predetermined area and the time interval
(reappearance time interval) until the extracted person reappears
in the predetermined area after leaving the predetermined area
(disappearing from the predetermined area) for each extracted
person. Then, the analysis apparatus infers the characteristic of
the extracted person on the basis of the continuous appearance time
period and the reappearance time interval. The characteristic of
the extracted person inferred on the basis of the continuous
appearance time period and the reappearance time interval is a kind
of information that can be recognized from context or the state of
the person. Examples of the characteristic include a traveler, a
passerby, a pickpocket, an operator, a migrant worker, a suspicious
person, a demonstrator, and a homeless person. Here, the examples
are illustrative and are not limited thereto. Hereinafter, this
exemplary embodiment will be described in detail.
[0036] FIG. 2 is an example of the functional block diagram of an
analysis apparatus 10 according to this exemplary embodiment. As
illustrated in FIG. 2, the analysis apparatus 10 includes a person
extraction unit 11, a time calculation unit 12, and an inference
unit 13.
[0037] The person extraction unit 11 analyzes video data and
extracts a person from the video data. Any technique can be used as
a process of extracting a person.
[0038] For example, video data which is captured by one or a
plurality of cameras (for example, surveillance cameras) installed
at predetermined positions is input to the person extraction unit
11. For example, the person extraction unit 11 processes the video
data in time series and extracts a person from the video data.
[0039] The person extraction unit 11 may process all of the frames
included in the video data or process may be performed on a basis
of each predetermined frame. Then, the person extraction unit 11
extracts a person from the frame which is a processing target. In
addition, the person extraction unit 11 extracts the feature amount
(for example, the feature amount of the face) of the outward
appearance of the extracted person.
[0040] The time calculation unit 12 calculates the continuous
appearance time period for which the person extracted by the person
extraction unit 11 has been continuously present in a predetermined
area and the reappearance time interval until the person reappears
in the predetermined area after leaving the predetermined area for
each extracted person. Any technique can be used to calculate the
continuous appearance time period and the reappearance time
interval. Hereinafter, an example will be described and the
invention is not limited thereto.
[0041] For example, the time period for which a person appears
continuously in the video data may be the continuous appearance
time period and the time interval until the person reappears after
disappearing from the video data may be the reappearance time
interval. That is, when an n-th frame to be processed is
represented by Fn, for example, it is assumed that a certain person
is extracted from each of frames F1 to F500, is not extracted from
each of frames F501 to F1500, and is extracted from a frame F1501
again. In this case, the time elapsed from the frame F1 to the
frame F500 may be the continuous appearance time period and the
time elapsed from the frame F501 to the frame F1501 may be the
reappearance time interval.
[0042] In this case, an area captured by the camera is the
predetermined area. For example, the continuous appearance time and
the reappearance time are calculated by the method detailed below,
and thus the predetermined area can be expanded to the area
captured by the camera and a peripheral area.
[0043] Here, it is assumed that video data captured by one camera
is a processing target. The time calculation unit 12 calculates an
elapsed time t from the extraction of a person A (first person)
from the video data to the next extraction of the person A from the
video data. Then, when the elapsed time t is less than a
predetermined time t.sub.s (a matter of design) (or when the
elapsed time t is equal to or less than the predetermined time
t.sub.s), it is determined that the first person has been
continuously present in the predetermined area for the elapsed time
t. On the other hand, when the elapsed time t is equal to or
greater than the predetermined time t.sub.s (or when the elapsed
time t is greater than the predetermined time t.sub.s), it is
determined that the first person has not been present in the
predetermined area for the elapsed time t.
[0044] A detailed example will be described with reference to FIG.
3. The elapsed time t1 from the first extraction of the person A to
the second extraction of the person A is less than the
predetermined time t.sub.s. Therefore, the time calculation unit 12
determines that the person A has been continuously present in the
predetermined area for the time (elapsed time t1) from the first
extraction to the second extraction.
[0045] An elapsed time t2 from the second extraction of the person
A to the third extraction of the person A is less than the
predetermined time t.sub.s. Therefore, the time calculation unit 12
determines that the person A has been continuously present in the
predetermined area for the time (elapsed time t2) from the second
extraction to the third extraction. Then, the time calculation unit
12 determines that the person A has been continuously present in
the predetermined area for the time (here, the time from the first
extraction to the third extract) for which the state that an
elapsed time is less than the predetermined time t.sub.s
continues.
[0046] An elapsed time t3 from the third extraction of the person A
to the fourth extraction of the person A is greater than the
predetermined time t.sub.s. Therefore, the time calculation unit 12
determines that the person A has not been present in the
predetermined area for the time (elapsed time t3) from the third
extraction to the fourth extraction. Then, the time calculation
unit 12 sets the elapsed time t3 as the reappearance time interval.
In addition, the time calculation unit 12 sets (t1+t2) as the
continuous appearance time period.
[0047] Another example will be described. Here, it is assumed that
video data captured by a plurality of cameras is a processing
target. All of the plurality of cameras capture the image of a
predetermined position in the same predetermined area. For example,
all of the plurality of cameras may be installed in the same area
of a ".smallcircle..smallcircle." park. The imaging areas captured
by the plurality of cameras may partially overlap each other or may
not overlap each other.
[0048] The time calculation unit 12 calculates the elapsed time t
from the extraction of the first person from video data captured by
a first camera to the next extraction of the first person from
video data by any camera (which may be the first camera or another
camera). When the elapsed time t is less than the predetermined
time t.sub.s (a matter of design) (or when the elapsed time t is
equal to or less than the predetermined time t.sub.s), it is
determined that the first person has been continuously present in
the predetermined area for the elapsed time t. On the other hand,
when the elapsed time t is equal to or greater than the
predetermined time t.sub.s (or when the elapsed time t is greater
than the predetermined time t.sub.s), it is determined that the
first person has not been present in the predetermined area for the
elapsed time t.
[0049] A detailed example will be described with reference to FIG.
4. It is assumed that the person A is extracted from video data
captured by a camera A (Cam A) (first extraction) and is then
extracted from video data captured by a camera B (Cam B) (second
extraction). The elapsed time t1 from the first extraction to the
second extraction is less than the predetermined time t.sub.s.
Therefore, the time calculation unit 12 determines that the person
A has been continuously present in the predetermined area for the
time (elapsed time t1) from the first extraction to the second
extraction.
[0050] The elapsed time t2 from the second extraction of the person
A from the video data captured by the camera B (Cam B) to the next
(third) extraction of the person A from video data captured by a
camera C (Cam C) is less than the predetermined time t.sub.s.
Therefore, the time calculation unit 12 determines that the person
A has been continuously present in the predetermined area for the
time (elapsed time t2) from the second extraction to the third
extraction. Then, the time calculation unit 12 determines that the
person A has been continuously present in the predetermined area
for the time (the time from the first extraction to the third
extract) for which the state that an elapsed time is less than the
predetermined time t.sub.s continues.
[0051] The elapsed time t3 from the third extraction of the person
A from the video data captured by the camera C (Cam C) to the next
(fourth) extraction of the person A from the video data captured by
the camera B (Cam B) is greater than the predetermined time
t.sub.s. Therefore, the time calculation unit 12 determines that
the person A has not been present in the predetermined area for the
time (elapsed time t3) from the third extraction to the fourth
extraction. Then, the time calculation unit 12 sets the elapsed
time t3 as the reappearance time interval. In addition, the time
calculation unit 12 sets a time (t1+t2) as the continuous
appearance time period.
[0052] Next, this exemplary embodiment will be described on the
assumption that the calculation method described with reference to
FIGS. 3 and 4 is used.
[0053] Incidentally, it is necessary to determine whether a person
extracted from a certain frame and a person extracted from the
previous frame are the same person, in order to perform the
above-mentioned process. All of pairs of the feature amounts of the
outward appearance of each person extracted from the previous frame
and the feature amounts of the outward appearance of each person
extracted from a certain frame may be compared to perform the
determination described above. However, in the case of this
process, as the accumulated data of persons increases, the number
of pairs to be compared increases and thus processing load
increases. Therefore, for example, the method described below may
be adopted.
[0054] For example, the extracted person may be indexed as
illustrated in FIG. 5 to determine whether a person is identical to
a previously extracted person by using the index. The use of the
index makes it possible to increase a processing speed. The details
of the index and a method for generating the index are disclosed in
the Patent Documents 2 and 3. Next, the structure of the index
illustrated in FIG. 5 and a method for using the index will be
described in brief.
[0055] An extraction identifier (ID)
"F.smallcircle..smallcircle..smallcircle.-.smallcircle..smallcircle..smal-
lcircle..smallcircle." illustrated in FIG. 5 is identification
information which is given to each person extracted from each
frame. "F.smallcircle..smallcircle..smallcircle." is frame
identification information and numbers following a hyphen are
identification information of each person extracted from each
frame. In a case in which the same person is extracted from
different frames, different extraction IDs are given to the
person.
[0056] In a third layer, nodes corresponding to all of the
extraction IDs obtained from the processed frames are arranged.
Among a plurality of nodes arranged in the third layer, nodes with
similarity (similarity between the feature amounts of the outward
appearance) that is equal to or higher than a first level are
grouped. In the third layer, a plurality of extraction IDs which
are determined to indicate the same person are grouped. That is,
the first level of the similarity is set to a value that can
implement the grouping. Person identification information (person
ID) is given so as to correspond to each group in the third
layer.
[0057] In a second layer, one node (representative) which is
selected from each of a plurality of groups in the third layer is
arranged and is associated with the group in the third layer. Among
a plurality of nodes arranged in the second layer, nodes with
similarity that is equal to or higher than a second level are
grouped. The second level of the similarity is lower than the first
level. That is, the nodes which are not grouped together on the
basis of the first level may be grouped together on the basis of
the second level.
[0058] In a first layer, one node (representative) which is
selected from each of a plurality of groups in the second layer is
arranged and is associated with the group in the second layer.
[0059] For example, the time calculation unit 12 indexes a
plurality of extraction IDs obtained by the above-mentioned process
as illustrated in FIG. 5.
[0060] Then, when a new extraction ID is obtained from a new frame,
the time calculation unit 12 determines whether a person
corresponding to the extraction ID is identical to a previously
extracted person using the information. In addition, the time
calculation unit 12 adds the new extraction ID to the index. Next,
this process will be described.
[0061] First, the time calculation unit 12 sets a plurality of
extraction IDs in the first layer as a comparison target. The
person extraction unit 11 makes a pair of the new extraction ID and
each of the plurality of extraction IDs in the first layer. Then,
the person extraction unit 11 calculates similarity (similarity
between the feature amounts of the outward appearance) for each
pair and determines whether the calculated similarity is equal to
or greater than a first threshold value (is equal to or higher than
a predetermined level).
[0062] In a case in which an extraction ID with similarity that is
equal to or greater than the first threshold value is not present
in the first layer, the time calculation unit 12 determines that
the person corresponding to the new extraction ID is not identical
to any previously extracted person. Then, the time calculation unit
12 adds the new extraction ID to the first to third layers and
associates them with each other. In the second and third layers, a
new group is generated by the added new extraction ID. In addition,
a new person ID is issued in correspondence with the new group in
the third layer. Then, the person ID is specified as the person ID
of the person corresponding to the new extraction ID.
[0063] On the other hand, in a case in which the extraction ID with
similarity that is equal to or greater than the first threshold
value is present in the first layer, the time calculation unit 12
changes a comparison target to the second layer. Specifically, the
group in the second layer associated with the "extraction ID in the
first layer which has been determined to have similarity equal to
or greater than the first threshold value" is set as a comparison
target.
[0064] Then, the time calculation unit 12 makes a pair of the new
extraction ID and each of a plurality of extraction IDs included in
the group to be processed in the second layer. Then, the time
calculation unit 12 calculates similarity for each pair and
determines whether the calculated similarity is equal to or greater
than a second threshold value. The second threshold value is
greater than the first threshold value.
[0065] In a case in which an extraction ID with similarity that is
equal to or greater than the second threshold value is not present
in the group to be processed in the second layer, the time
calculation unit 12 determines that the person corresponding to the
new extraction ID is not identical to any previously extracted
person. Then, the time calculation unit 12 adds the new extraction
ID to the second and third layers and associates them with each
other. In the second layer, the new extraction ID is added to the
group to be processed. In the third layer, a new group is generated
by the added new extraction ID. In addition, a new person ID is
issued in correspondence with the new group in the third layer.
Then, the time calculation unit 12 specifies the person ID as the
person ID of the person corresponding to the new extraction ID.
[0066] On the other hand, in a case in which the extraction ID with
similarity that is equal to or greater than the second threshold
value is present in the group to be processed in the second layer,
the time calculation unit 12 determines that the person
corresponding to the new extraction ID is identical to a previously
extracted person. Then, the time calculation unit 12 puts the new
extraction ID into the group in the third layer associated with the
"extraction ID in the second layer which has been determined to
have similarity equal to or greater than the second threshold
value". In addition, the time calculation unit 12 determines the
person ID corresponding to the group in the third layer as the
person ID of the person corresponding to the new extraction ID.
[0067] For example, in this way, it is possible to associate a
person ID with one extraction ID or each of a plurality of
extraction IDs extracted from a new frame.
[0068] For example, the time calculation unit 12 may manage
information illustrated in FIG. 6 for each extracted person ID.
Then, the time calculation unit 12 may calculate the continuous
appearance time period and the reappearance time interval, using
the information. In the information illustrated in FIG. 6, the
person ID, the continuous appearance time period, and the latest
extraction timing are associated with each other.
[0069] The values of the continuous appearance time period and the
latest extraction timing are updated as needed. For example, when a
certain person is extracted from the video data first and a new
person ID is added to the information illustrated in FIG. 6, "0" is
recorded as the continuous appearance time period. In addition, the
extraction timing is recorded as the latest extraction timing. The
extraction timing is represented by, for example, date and time
information. The extraction timing in this example corresponds to,
for example, the first extraction illustrated in FIG. 3. Then, the
time calculation unit 12 waits for the next extraction.
[0070] Then, in a case in which the person is extracted for the
second time, it is determined whether the person has been
continuously present for the elapsed time t1 on the basis of the
result of the large and small comparison between the elapsed time
t1 and the predetermined time t.sub.s, as described above. The
elapsed time t1 is calculated on the basis of, for example, the
value in the field of latest extraction timing and the extraction
timing of the second time. In a case in which it is determined that
the person has been present, the value of the continuous appearance
time period is updated. Specifically, the sum of the value recorded
at that time and the elapsed time t1 is recorded in the field.
Here, t1 (=0+t1) is recorded. Then, the latest extraction timing is
updated to the extraction timing of the second time. Then, the time
calculation unit 12 waits for the next extraction.
[0071] Then, in a case in which the person is extracted for the
third time, it is determined whether the person has been
continuously present for the elapsed time t2 on the basis of the
result of the large and small comparison between the elapsed time
t2 and the predetermined time t.sub.s, as described above. The
elapsed time t2 is calculated on the basis of, for example, the
value in the field of latest extraction timing and the extraction
timing of the third time. In a case in which it is determined that
the person has been present, the value of the continuous appearance
time period is updated. Specifically, the sum (t1+t2) of the value
(t1) recorded at that time and the elapsed time t2 is recorded in
the field. Then, the latest extraction timing is updated to the
extraction timing of the third time. Then, the time calculation
unit 12 waits for the next extraction.
[0072] Then, in a case in which the person is extracted for the
fourth time, it is determined whether the person has been
continuously present for the elapsed time t3 on the basis of the
result of the large and small comparison between the elapsed time
t3 and the predetermined time t.sub.s, as described above. The
elapsed time t3 is calculated on the basis of, for example, the
value in the field of latest extraction timing and the extraction
timing of the fourth time. In a case in which it is determined that
the person has not been present, the value of the continuous
appearance time period at that time is fixed as the continuous
appearance time period of the person. In addition, the elapsed time
t3 is fixed as the reappearance time interval of the person. Then,
a pair of the fixed continuous appearance time period and the fixed
reappearance time interval is input to the inference unit 13.
[0073] In addition, the value of the continuous appearance time
period is updated. Specifically, "0" is recorded in the field.
Then, the latest extraction timing is updated to the extraction
timing of the fourth time. Then, the time calculation unit 12 waits
for the next extraction. Then, the same process as described above
is repeated.
[0074] Returning to FIG. 2, the inference unit 13 infers the
characteristic of the extracted person on the basis of the
continuous appearance time period and the reappearance time
interval. The inference unit 13 infers the characteristic of the
person on the basis of the relationship between the continuous
appearance time period and the reappearance time interval. The
inference unit 13 infers the characteristic of the person on the
basis of the pair of the continuous appearance time period and the
reappearance time interval input from the time calculation unit
12.
[0075] For example, the inference unit 13 may infer the
characteristic of the person (hereinafter, referred to as a
personal characteristic in some cases) on the basis of
correspondence information (correspondence information indicating
the relationship between the continuous appearance time period and
the reappearance time interval) in which the pair of the continuous
appearance time period and the reappearance time interval is
associated with the inferred characteristic.
[0076] FIG. 7 illustrates an example of the correspondence
information. The correspondence information is represented by a
graph in which one axis (the horizontal axis in FIG. 7) indicates
the continuous appearance time period and the other axis (the
vertical axis in FIG. 7) indicates the reappearance time interval.
An area on the graph is divided into a plurality of areas and a
personal characteristic is associated with each area. The divided
areas illustrated in FIG. 7 or the personal characteristic
associated with each area are exemplified as a conceptual diagram
for illustrating the invention and the correspondence information
is not limited to the content.
[0077] In a case in which the correspondence information is used,
the inference unit 13 determines which personal characteristic area
the pair of the continuous appearance time period and the
reappearance time interval is located in, as illustrated in FIG. 7,
and infers the personal characteristic on the basis of the
determination result.
[0078] As another example, the inference unit 13 may infer the
personal characteristic on the basis of the correspondence
information having different contents for each time slot during
which a person appears.
[0079] That is, as illustrated in FIGS. 8 and 9, the inference unit
13 may store correspondence information for each time slot. FIG. 8
corresponds to a time slot from 4 a.m. to 10 p.m. and FIG. 9
corresponds to a time slot from 10 p.m. to 4 a.m. As can be seen
from the comparison between the correspondence information items
illustrated in FIGS. 8 and 9, the contents of the correspondence
information items are different from each other. The inference unit
13 may determine one correspondence information item based on the
time slot during which the extracted person appears and may infer
the personal characteristic on the basis of the determined
correspondence information item, using the same method as described
above.
[0080] For example, the inference unit 13 may use correspondence
information corresponding to a time slot including a representative
timing for the period of time for which the extracted person
appears. The representative timing may be, for example, the timing
(the first extraction timing in the example illustrated in FIG. 3)
when the person is extracted first, the last extraction timing (the
third extraction timing in the example illustrated in FIG. 3), an
intermediate timing between the first extraction timing and the
last extraction timing, or other timings.
[0081] In addition, the inference unit 13 may calculate the
overlapping period between the time slot corresponding to each
correspondence information item and the time period for which the
person appears. Then, the inference unit 13 may use the
correspondence information corresponding to the longer overlapping
period. For example, in a case in which the appearance period is
from 2 a.m. to 5 a.m., the overlapping period between the time slot
(from 4 a.m. to 10 p.m.) corresponding to the correspondence
information illustrated in FIG. 8 and the appearance period is 1
hour from 4 a.m. to 5 a.m. In contrast, the overlapping period
between the time slot (from 10 p.m. to 4 a.m.) corresponding to the
correspondence information illustrated in FIG. 9 and the appearance
period is 2 hours from 2 a.m. to 4 a.m. In this case, the inference
unit 13 may use the correspondence information illustrated in FIG.
9.
[0082] In the above-mentioned example, two correspondence
information items corresponding to two time slots are used.
However, the number of correspondence information items is a matter
of design and is not limited thereto.
[0083] Furthermore, the inference unit 13 may infer the personal
characteristic on the basis of the continuous appearance time
period, the reappearance time interval, data indicating a
probability distribution which is stored in advance, and the
correspondence information.
[0084] For example, as illustrated in FIG. 10, the inference unit
13 sets a point corresponding to the pair of the continuous
appearance time period and the reappearance time interval as a peak
position of the probability distribution. Then, the inference unit
13 extracts all personal characteristics corresponding to the area
including a portion in which probability is greater than 0. In
addition, the inference unit 13 calculates probability
corresponding to each personal characteristic on the basis of the
data of the probability distribution. For example, the inference
unit 13 may calculate a statistic (for example, a maximum value and
an intermediate value) in the probability included in each area as
the probability corresponding to the personal characteristic.
[0085] The analysis apparatus 10 may include a notification unit,
which is not illustrated in FIG. 2. When an extracted person is
inferred to be a predetermined personal characteristic (for
example, a suspicious person), the notification unit notifies an
operator of the person and information indicating that the
extracted person is inferred to be the predetermined personal
characteristic. The notification can be implemented through all
types of output devices such as a display, an emailer, a speaker,
an alarm lamp, and a printer. In the notification process, the
notification unit may notify an operator of the image of the face
of the person.
[0086] The analysis apparatus 10 may include a storage unit that
stores the inference result of the inference unit 13 and an output
unit that outputs the inference result, which is not illustrated in
FIG. 2. The storage unit stores the person ID and the inference
result in association with each other. The storage unit may store
one or a plurality of image data items (image data including the
person) in association with the person ID. In addition, the storage
unit may store the timing (date and time information) when each
person appears.
[0087] Then, the output unit may acquire predetermined information
from the storage unit and output the predetermined information, in
accordance with an operation of the operator. For example, when an
input specifying the personal characteristic is received, a list of
the persons corresponding to the personal characteristic may be
displayed. In addition, when an input specifying the personal
characteristic and a period is received, a list of the persons who
have been inferred to be the personal characteristic within the
period may be displayed. The display of the list may be
implemented, using image data corresponding to each person.
[0088] Next, an example of the flow of the process of the analysis
apparatus 10 according to this exemplary embodiment will be
described with reference to the flowchart illustrated in FIG.
11.
[0089] In a person extraction step S10, the person extraction unit
11 analyzes video data to extract a person.
[0090] In a continuous appearance time period and reappearance time
interval calculation step S11, the time calculation unit 12
calculates the continuous appearance time period for which each
person extracted in S10 has been continuously present and the
reappearance time interval until the person reappears in a
predetermined area after leaving the predetermined area.
[0091] In a personal characteristic inference step S12, the
inference unit 13 infers the characteristic of the extracted person
on the basis of the continuous appearance time period and the
reappearance time interval calculated in S11.
[0092] According to the above-described exemplary embodiment, it is
possible to infer the personal characteristic on the basis of the
continuous appearance time period for which a person has been
continuously present in a predetermined area and the reappearance
time interval until the person reappears after leaving the
predetermined area. That is, it is possible to infer the personal
characteristic on the basis of new information such as the
reappearance time interval. Therefore, for example, the accuracy of
inference is expected to be improved and an inference technique is
expected to progress.
[0093] According to this exemplary embodiment, it is possible to
infer the personal characteristic on the basis of a pair of the
continuous appearance time period and the reappearance time
interval. In the case of this exemplary embodiment, the personal
characteristic is inferred not on the basis of the criterion that
"as the continuous appearance time period increases, the
possibility that a person is a suspicious person increases".
However, in a case in which the position (a position in a
two-dimensional coordinate illustrated in FIG. 7) of a pair of the
value of the continuous appearance time period and the value of the
reappearance time interval is included in a predetermined range,
the person is inferred to be a certain personal characteristic (for
example, a suspicious person). As such, by inferring the personal
characteristic on the basis of a plurality of information items
(the continuous appearance time period and the reappearance time
interval), it is possible to improve the accuracy of inference.
[0094] According to this exemplary embodiment, it is possible to
infer the personal characteristic on the basis of a plurality of
correspondence information items with different contents for each
time slot during which a person appears. It is inferred that there
is a large difference in personal characteristics between a person
who appears during the day and a person who appears during the
night. Since the personal characteristic is inferred considering
the appearance timing, it is possible to improve the accuracy of
inference.
[0095] According to this exemplary embodiment, it is possible to
infer the personal characteristic, using a probability
distribution. The output result of the inference unit 13 is just
the inference result and is not 100 percent accurate. Therefore,
there is the possibility that the person, who is essentially to be
inferred as a suspicious person, is inferred as another personal
characteristic such as a traveler. The inference of possible
personal characteristics using probability distributions enables a
wide inference of possible personal characteristics. For example,
in the case of the above-mentioned example, in addition to a
traveler, a suspicious person can be inferred as a possible
personal characteristic.
[0096] According to this exemplary embodiment, the continuous
appearance time period and the reappearance time interval can be
calculated by the method described with reference to FIGS. 3 and 4.
In the case of this calculation method, the area captured by the
camera and the periphery of the area are set as a predetermined
area and the continuous appearance time period and the reappearance
time interval can be calculated for the predetermined area. That
is, it is possible to expand the predetermined area to an area
which is not captured by the camera.
[0097] As a modification example of this exemplary embodiment, in
the correspondence information, not all pairs of the continuous
appearance time period and the reappearance time interval are
necessarily associated with personal characteristics as illustrated
in, for example, FIG. 7. For example, the correspondence
information may include only some personal characteristics (for
example, a suspicious person and a pickpocket) of the information
illustrated in FIG. 7. In using such correspondence information, in
a case in which the values of the continuous appearance time period
and the reappearance time interval correspond to the personal
characteristics (for example, a suspicious person and a
pickpocket), the personal characteristic is inferred. On the other
hand, in a case in which the values of the continuous appearance
time period and the reappearance time interval correspond to
neither of the personal characteristics (for example, a suspicious
person and a pickpocket), the person is inferred not to be the
personal characteristics. This modification example can also be
applied to all of the exemplary embodiments described below.
Second Exemplary Embodiment
[0098] An analysis apparatus 10 according to this exemplary
embodiment stores the personal characteristic inferred by the
method described in the first exemplary embodiment in association
with each person. In a case in which a certain person appears
repeatedly in a predetermined area, the analysis apparatus 10
calculates the continuous appearance time period and the
reappearance time interval whenever the person appears. On all such
occasion, on the basis of the calculation result, a personal
characteristic is inferred. On the basis of the inference result,
the analysis apparatus 10 counts the number of times each personal
characteristic is inferred, for each person. Then, the analysis
apparatus 10 calculates the reliability of each inferred personal
characteristic on the basis of the number of counts. Hereinafter,
this exemplary embodiment will be described in detail.
[0099] FIG. 12 illustrates an example of the functional block
diagram of the analysis apparatus 10 according to this exemplary
embodiment. As illustrated in FIG. 12, the analysis apparatus 10
includes a person extraction unit 11, a time calculation unit 12,
an inference unit 13, a count unit 14, and a reliability
calculation unit 15. The analysis apparatus 10 may further include
the notification unit, the storage unit, and the output unit
described in the first exemplary embodiment, which is not
illustrated. The person extraction unit 11, the time calculation
unit 12, the inference unit 13, the notification unit, the storage
unit, and the output unit have the same structure as those in the
first exemplary embodiment.
[0100] The count unit 14 counts the number of times each personal
characteristic which is inferred in correspondence with each person
is inferred.
[0101] For example, the count unit 14 manages information
illustrated in FIG. 13. In the information illustrated in FIG. 13,
a person ID is associated with the number of times each personal
characteristic is inferred. The count unit 14 updates the
information on the basis of the inference result of the inference
unit 13.
[0102] Whenever a certain person appears repeatedly in a
predetermined area, the time calculation unit 12 calculates the
continuous appearance time period and the reappearance time
interval. On all such occasion, the inference unit 13 infers the
personal characteristic on the basis of the continuous appearance
time period and the reappearance time interval calculated whenever
the certain person appears repeatedly in the predetermined
area.
[0103] The count unit 14 updates the information illustrated in
FIG. 13 on the basis of the result inferred by the inference unit
13 in such a way.
[0104] The reliability calculation unit 15 calculates the
reliability of the inferred personal characteristic on the basis of
the number of times each personal characteristic which is inferred
in correspondence with a certain person is inferred. The larger the
number of times of inference is, the higher the reliability is
calculated by the reliability calculation unit 15.
[0105] The output unit may output predetermined information on the
basis of the inference result of the inference unit 13 and the
calculation result of the reliability calculation unit 15. The
output unit can output the predetermined information in accordance
with an operation of the operator.
[0106] For example, when an input specifying a personal
characteristic and reliability conditions (for example, reliability
is equal to or higher than a predetermined level) is received, a
list of the persons who are inferred to be the personal
characteristic with reliability equal to or higher than the
predetermined level may be displayed. The display of the list may
be implemented using image data corresponding to each person.
[0107] According to the above-described exemplary embodiment, it is
possible to obtain the same advantageous effect as that in the
first exemplary embodiment. In addition, it is possible to
calculate the reliability of each inferred personal characteristic
on the basis of many inference results which are stored in
correspondence with each person. As a result, according to this
exemplary embodiment, it is possible to improve the accuracy of
inferring the personal characteristic of each person.
Third Exemplary Embodiment
[0108] An analysis apparatus 10 according to this exemplary
embodiment provides a function of setting correspondence
information in which a pair of the continuous appearance time
period and the reappearance time interval is associated with a
personal characteristic.
[0109] FIG. 14 illustrates an example of the functional block
diagram of the analysis apparatus 10 according to this exemplary
embodiment. As illustrated in FIG. 14, the analysis apparatus 10
includes a person extraction unit 11, a time calculation unit 12,
an inference unit 13, and a setting unit 16. The analysis apparatus
10 may further include a count unit 14, a reliability calculation
unit 15, a notification unit, a storage unit, and an output unit,
which are not illustrated in FIG. 14. The person extraction unit
11, the time calculation unit 12, the count unit 14, the
reliability calculation unit 15, the notification unit, the storage
unit, and the output unit have the same structure as those in the
first and second exemplary embodiments.
[0110] The setting unit 16 has a function of setting the
correspondence information in which a pair of the continuous
appearance time period and the reappearance time interval is
associated with a personal characteristic. The setting unit 16 can
set the correspondence information in accordance with an input from
the user.
[0111] For example, the setting unit 16 may output a setting screen
illustrated in FIG. 15 through an output device such as a display.
The setting screen is a screen for receiving input of the name of a
personal characteristic, the start time and end time of the
continuous appearance time period, and the start time and end time
of the reappearance time interval.
[0112] In addition, the setting unit 16 may output a setting screen
illustrated in FIG. 16 through an output device such as a display.
The setting screen is a screen for receiving specification of a
predetermined area on a graph in which one axis (the horizontal
axis in FIG. 16) indicates the continuous appearance time period
and the other axis (the vertical axis in FIG. 16) indicates the
reappearance time interval and an input of the name of the personal
characteristic corresponding to the area.
[0113] The inference unit 13 infers a personal characteristic on
the basis of the correspondence information set by the setting unit
16. The other structures of the inference unit 13 are the same as
those in the first and second exemplary embodiments.
[0114] According to the above-described exemplary embodiment, it is
possible to obtain the same advantageous effect as that in the
first and second exemplary embodiments. In addition, it is possible
to freely set various personal characteristics. By setting a
personal characteristic of a person to be detected in the video
data, it is possible to detect a person with the personal
characteristic.
[0115] Hereinafter, an example of reference exemplary embodiments
will be additionally described.
[0116] 1. An analysis apparatus including: a person extraction unit
that analyzes video data to extract a person; a time calculation
unit that calculates a continuous appearance time period for which
the extracted person has been continuously present in a
predetermined area and a reappearance time interval until the
extracted person reappears in the predetermined area for each
extracted person; and an inference unit that infers a
characteristic of the extracted person on the basis of the
continuous appearance time period and the reappearance time
interval.
[0117] 2. The analysis apparatus described in 1, in which the
inference unit infers the characteristic of the person on the basis
of a relationship between the continuous appearance time period and
the reappearance time interval.
[0118] 3. The analysis apparatus described in 1 or 2 further
including: a count unit that counts the number of times each
characteristic which is inferred in correspondence with each person
is inferred; and a reliability calculation unit that calculates
reliability of the inferred characteristic on the basis of the
number of times each characteristic which is inferred in
correspondence with a certain person is inferred.
[0119] 4. The analysis apparatus described in any one of 1 to 3, in
which the inference unit infers the characteristic of the person on
the basis of correspondence information in which a pair of the
continuous appearance time period and the reappearance time
interval is associated with a characteristic.
[0120] 5. The analysis apparatus described in 4, in which the
inference unit infers the characteristic of the person on the basis
of the correspondence information having different contents for
each time slot during which the person appears.
[0121] 6. The analysis apparatus described in 4 or 5, in which the
inference unit infers the characteristic of the person on the basis
of the continuous appearance time period, the reappearance time
interval, a probability distribution, and the correspondence
information.
[0122] 7. The analysis apparatus described in any one of 1 to
6,
[0123] in which, in a case in which a time t elapsed from the
extraction of a first person from the video data to the next
extraction of the first person from the video data is less than a
predetermined time t.sub.s, the time calculation unit determines
that the first person has been continuously present in the
predetermined area for the elapsed time t, and
[0124] in a case in which the elapsed time t is equal to or greater
than the predetermined time t.sub.s, the time calculation unit
determines that the first person has not been present in the
predetermined area for the elapsed time t.
[0125] 8. An analysis method performed by a computer including: a
person extraction step of analyzing video data to extract a person;
a time calculation step of calculating a continuous appearance time
period for which the extracted person has been continuously present
in a predetermined area and a reappearance time interval until the
extracted person reappears in the predetermined area for each
extracted person; and an inference step of inferring a
characteristic of the extracted person on the basis of the
continuous appearance time period and the reappearance time
interval.
[0126] 8-2. The analysis method described in 8, in which in the
inference step, the characteristic of the person is inferred on the
basis of a relationship between the continuous appearance time
period and the reappearance time interval.
[0127] 8-3. The analysis method performed by the computer described
in 8 or 8-2, the method further including: a count step of counting
the number of times each characteristic which is inferred in
correspondence with each person is inferred; and a reliability
calculation step of calculating reliability of the inferred
characteristic on the basis of the number of times each
characteristic which is inferred in correspondence with a certain
person is inferred.
[0128] 8-4. The analysis method described in any one of 8 to 8-3,
in which in the inference step, the characteristic of the person is
inferred on the basis of correspondence information in which a pair
of the continuous appearance time period and the reappearance time
interval is associated with a characteristic.
[0129] 8-5. The analysis method described in 8-4, in which in the
inference step, the characteristic of the person is inferred on the
basis of the correspondence information having different contents
for each time slot during which the person appears.
[0130] 8-6. The analysis method described in 8-4 or 8-5, in which
in the inference step, the characteristic of the person is inferred
on the basis of the continuous appearance time period, the
reappearance time interval, a probability distribution, and the
correspondence information.
[0131] 8-7. The analysis method described in any one of 8 to 8-6,
in which in the time calculation step,
[0132] in a case in which a time t elapsed from the extraction of a
first person from the video data to the next extraction of the
first person from the video data is less than a predetermined time
t.sub.s, it is determined that the first person has been
continuously present in the predetermined area for the elapsed time
t, and
[0133] in a case in which the elapsed time t is equal to or greater
than the predetermined time t.sub.s, it is determined that the
first person has not been present in the predetermined area for the
elapsed time t.
[0134] 9. A program causing a computer to function as: a person
extraction unit that analyzes video data to extract a person; a
time calculation unit that calculates a continuous appearance time
period for which the extracted person has been continuously present
in a predetermined area and a reappearance time interval until the
extracted person reappears in the predetermined area for each
extracted person; and an inference unit that infers a
characteristic of the extracted person on the basis of the
continuous appearance time period and the reappearance time
interval.
[0135] 9-2. The program described in 9, in which the inference unit
infers the characteristic of the person on the basis of a
relationship between the continuous appearance time period and the
reappearance time interval.
[0136] 9-3. The program described in 9 or 9-2 causing the computer
to further function as: a count unit that counts the number of
times each characteristic which is inferred in correspondence with
each person is inferred; and a reliability calculation unit that
calculates reliability of the inferred characteristic on the basis
of the number of times each characteristic which is inferred in
correspondence with a certain person is inferred.
[0137] 9-4. The program described in any one of 9 to 9-3, in which
the inference unit infers the characteristic of the person on the
basis of correspondence information in which a pair of the
continuous appearance time period and the reappearance time
interval is associated with a characteristic.
[0138] 9-5. The program described in 9-4, in which the inference
unit infers the characteristic of the person on the basis of the
correspondence information having different contents for each time
slot during which the person appears.
[0139] 9-6. The program described in 9-4 or 9-5, in which the
inference unit infers the characteristic of the person on the basis
of the continuous appearance time period, the reappearance time
interval, a probability distribution, and the correspondence
information.
[0140] 9-7. The program described in any one of 9 to 9-6,
[0141] in which, in a case in which a time t elapsed from the
extraction of a first person from the video data to the next
extraction of the first person from the video data is less than a
predetermined time t.sub.s, the time calculation unit determines
that the first person has been continuously present in the
predetermined area for the elapsed time t, and
[0142] in a case in which the elapsed time t is equal to or greater
than the predetermined time t.sub.s, the time calculation unit
determines that the first person has not been present in the
predetermined area for the elapsed time t.
[0143] It is apparent that the present invention is not limited to
the above exemplary embodiment, and may be modified and changed
without departing from the scope and spirit of the invention.
[0144] This application claims priority based on Japanese Patent
Application No. 2016-067538 filed on Mar. 30, 2016, the disclosure
of which is incorporated herein in its entirety.
* * * * *