U.S. patent application number 15/718113 was filed with the patent office on 2018-09-06 for crowd detection, analysis, and categorization.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Silpi Dhua, Anil M. Omanwar, Sujoy Sett, Pradip A. Waykos.
Application Number | 20180253606 15/718113 |
Document ID | / |
Family ID | 63355736 |
Filed Date | 2018-09-06 |
United States Patent
Application |
20180253606 |
Kind Code |
A1 |
Dhua; Silpi ; et
al. |
September 6, 2018 |
CROWD DETECTION, ANALYSIS, AND CATEGORIZATION
Abstract
A method, computer system, and a computer program product for
analyzing a crowd using a plurality of images captured by an aerial
drone is provided. The present invention may include determining a
geographic area associated with the crowd. The present invention
may also include partitioning the determined geographic area into a
plurality of zones. The present invention may then include
determining a flight path covering each zone within the plurality
of zones. The present invention may further include receiving the
plurality of images from the aerial drone. The present invention
may also include analyzing the received plurality of images to
identify a plurality of individuals associated with the crowd. The
present invention may then include predicting a plurality of crowd
characteristics based on the analyzed plurality of images. The
present invention may further include performing an action in
response to the predicted plurality of crowd characteristics.
Inventors: |
Dhua; Silpi; (Bankura,
IN) ; Omanwar; Anil M.; (Pune, IN) ; Sett;
Sujoy; (Kolkata, IN) ; Waykos; Pradip A.;
(Chikhli, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
ARMONK |
NY |
US |
|
|
Family ID: |
63355736 |
Appl. No.: |
15/718113 |
Filed: |
September 28, 2017 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
15448848 |
Mar 3, 2017 |
|
|
|
15718113 |
|
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0241 20130101;
G06K 9/4642 20130101; G06F 16/583 20190101; B64C 2201/127 20130101;
G06K 9/3233 20130101; G05D 1/0094 20130101; B64C 39/024 20130101;
G08G 5/0069 20130101; G06K 9/00778 20130101 |
International
Class: |
G06K 9/00 20060101
G06K009/00; G08G 5/00 20060101 G08G005/00; B64D 47/08 20060101
B64D047/08; G05D 1/00 20060101 G05D001/00; B64C 39/02 20060101
B64C039/02 |
Claims
1. A method for analyzing a crowd using a plurality of images
captured by an aerial drone, the method comprising: determining a
geographic area associated with the crowd; partitioning the
determined geographic area into a plurality of zones; determining a
flight path covering each zone within the plurality of zones;
sending the determined flight path to the aerial drone; flying, by
the aerial drone, along the sent flight path; generating, by the
aerial drone, the plurality of images, and a plurality of drone
position data that comprises a drone location, a drone orientation,
and a photographed location; receiving the plurality of images and
the plurality of drone position data from the aerial drone;
analyzing the received plurality of images to identify a plurality
of individuals associated with the crowd, wherein analyzing the
received plurality of images to identify the plurality of
individuals associated with the crowd further comprises cropping
each image within the received plurality of images to create an
image partition for each identified individual within the
identified plurality of individuals; determining an individual
position for each identified individual within the identified
plurality of individuals based on the analyzed plurality of images
and the received plurality of drone position data; determining an
individual movement vector for each identified individual within
the identified plurality of individuals based on tracking changes
in the determined individual position associated with the
individual as captured within the analyzed plurality of images;
predicting a plurality of individual characteristics for each
identified individual based on the analyzed plurality of images,
wherein predicting the plurality of individual characteristics
comprises using a pre-trained machine learning model to process the
analyzed plurality of images and predict the plurality of
individual characteristics, and wherein the predicted plurality of
individual characteristics includes a plurality of demographic
characteristics and a plurality of physical characteristics;
predicting a plurality of crowd characteristics based on the
predicted plurality of individual characteristics for the plurality
of identified individuals, wherein the predicted plurality of crowd
characteristics includes an aggregate motion characteristic
determined based on the determined individual movement vector for
each identified individual and a rate of entry and a rate of exit
corresponding with each zone within the plurality of zones, and
wherein the predicted plurality of crowd characteristics includes a
crowd concentration characteristic based on the determined
individual position for each identified individual; and determining
a placement and a duration for dynamic advertising to distribute to
the crowd based on the predicted plurality of crowd
characteristics.
Description
BACKGROUND
[0001] The present invention relates generally to the field of
computing, and more particularly to crowd analysis.
[0002] Photography and videography-based applications have gained
popularity in the past decade for administrative and security
purposes resulting in the prevalence of security cameras and
equipment in public areas. Existing applications may mostly be
based on fixed imagery angles from pre-aligned cameras or cameras
with limited movement. Furthermore, camera output may require
manual observation to derive information or to make decisions.
SUMMARY
[0003] Embodiments of the present invention disclose a method,
computer system, and a computer program product for analyzing a
crowd using a plurality of images captured by an aerial drone. The
present invention may include determining a geographic area
associated with the crowd. The present invention may also include
partitioning the determined geographic area into a plurality of
zones. The present invention may then include determining a flight
path covering each zone within the plurality of zones. The present
invention may also include sending the determined flight path to
the aerial drone. The present invention may further include
receiving the plurality of images from the aerial drone. The
present invention may also include analyzing the received plurality
of images to identify a plurality of individuals associated with
the crowd. The present invention may then include predicting a
plurality of crowd characteristics based on the analyzed plurality
of images. The present invention may further include performing an
action in response to the predicted plurality of crowd
characteristics.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0004] These and other objects, features and advantages of the
present invention will become apparent from the following detailed
description of illustrative embodiments thereof, which is to be
read in connection with the accompanying drawings. The various
features of the drawings are not to scale as the illustrations are
for clarity in facilitating one skilled in the art in understanding
the invention in conjunction with the detailed description. In the
drawings:
[0005] FIG. 1 illustrates a networked computer environment
according to at least one embodiment;
[0006] FIG. 2 illustrates a networked aerial image capture device
system according to at least one embodiment;
[0007] FIG. 3 is an operational flowchart illustrating a process
for crowd analysis according to at least one embodiment;
[0008] FIG. 4 is a block diagram of internal and external
components of computers and servers depicted in FIG. 1 according to
at least one embodiment;
[0009] FIG. 5 is a block diagram of an illustrative cloud computing
environment including the computer system depicted in FIG. 1, in
accordance with an embodiment of the present disclosure; and
[0010] FIG. 6 is a block diagram of functional layers of the
illustrative cloud computing environment of FIG. 5, in accordance
with an embodiment of the present disclosure.
DETAILED DESCRIPTION
[0011] Detailed embodiments of the claimed structures and methods
are disclosed herein; however, it can be understood that the
disclosed embodiments are merely illustrative of the claimed
structures and methods that may be embodied in various forms. This
invention may, however, be embodied in many different forms and
should not be construed as limited to the exemplary embodiments set
forth herein. Rather, these exemplary embodiments are provided so
that this disclosure will be thorough and complete and will fully
convey the scope of this invention to those skilled in the art. In
the description, details of well-known features and techniques may
be omitted to avoid unnecessarily obscuring the presented
embodiments.
[0012] The present invention may be a system, a method, and/or a
computer program product at any possible technical detail level of
integration. The computer program product may include a computer
readable storage medium (or media) having computer readable program
instructions thereon for causing a processor to carry out aspects
of the present invention.
[0013] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0014] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0015] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, configuration data for integrated
circuitry, or either source code or object code written in any
combination of one or more programming languages, including an
object oriented programming language such as Smalltalk, C++, or the
like, and procedural programming languages, such as the "C"
programming language or similar programming languages. The computer
readable program instructions may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider). In some embodiments,
electronic circuitry including, for example, programmable logic
circuitry, field-programmable gate arrays (FPGA), or programmable
logic arrays (PLA) may execute the computer readable program
instructions by utilizing state information of the computer
readable program instructions to personalize the electronic
circuitry, in order to perform aspects of the present
invention.
[0016] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0017] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0018] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0019] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0020] Photography and videography-based applications have gained
popularity in the past decade for administrative and security
purposes resulting in the prevalence of security cameras and
equipment in public areas. Existing applications may mostly be
based on fixed imagery angles from pre-aligned cameras or cameras
with limited movement. Furthermore, camera output may require
manual observation to derive information or to make decisions.
Therefore, it may be advantageous to, among other things, provide a
way to monitor crowds of people using a mobile image capturing
device capable of capturing images from an altitude, such as an
aircraft with a camera, and automatically analyze the camera output
to make decisions in response to the characteristics of the
crowd.
[0021] The following described exemplary embodiments provide a
system, method and program product for monitoring a crowd using a
device for capturing images from an altitude, such as aircraft and
aerial unmanned vehicles (UAVs) or aerial drones. As such, the
present embodiment has the capacity to improve the technical field
of crowd analysis by monitoring crowds from images taken by aerial
drones that may dynamically traverse above an area and make
decisions in real-time. More specifically, a still image camera and
a video camera mounted to the bottom of a controlled flight-capable
aerial drone or other aircraft that may also have the capability to
wirelessly transmit data of the generated images and video of a
group of persons forming a crowd. From the static images taken by
the drone, an image partitioning algorithm may be used to identify
individuals within the crowd images. Then a pre-trained model may
be used to predict the gender, age, socioeconomic status, or other
attributes of the individuals from the partitioned images. From the
captured video, an image partitioning algorithm may be used to
identify individuals and thereby determine the rate of entry and
rate of exit from a certain defined geographic area. Additionally,
motion points for individuals may be plotted to determine the
distribution of the mass of people gathering from the overhead
video. Based on predicted crowd characteristics drawn from
predicted individual characteristics, decisions may be made in
real-time for advertising, security, or some other purpose.
[0022] The present embodiment may be used to derive insights from a
mass gathering of people by categorizing the mass of people and
analyzing the motion of the mass by zoning a geographic area.
Drones equipped with flight and imagery capabilities may be used to
generate the data for analyzing the mass of people. By using
aircraft, overhead images may be generated that provide a better
position to assess the characteristics of the crowd and track
individuals within the crowd. Furthermore, aircraft may have fewer
obstacles to navigate around to capture images of the crowd. The
present embodiment may be useful for security enforcement by
reducing the number of personnel that may need to be deployed to
monitor crowds. The present embodiment may also help advertisers to
analyze a target mass of individuals and perform categorized
advertisement that may be more effective given the derived crowd
characteristics.
[0023] According to at least one embodiment, the operational area
over a mass gathering is first determined. Then, the target
geographical area may be defined by a polygon with coordinates.
Next, the drone takes the area as input and partitions the area
into smaller zones. The drone then traverses the area using a
traversing algorithm to move and position the drone over the zones
according to the algorithm. For each zone, the drone captures
images (e.g., 2-5 images) at a predefined time interval (e.g., 2-10
seconds). Thereafter, image processing steps may be performed for
each of the images in the sequence of captured images. Images may
be partitioned to create top-level images of each individual within
the crowd. A trained model may then process the partitioned images
to predict features (e.g., gender, age, and direction the person is
facing) of every individual. Best match algorithms may then be used
to identify every individual across the sequence of captured
images. From the sequential images or from captured video, the
motion direction of each individual may be determined. Then,
aggregate motion information, categorization, and concentration of
the entire crowd may be determined from captured still images or
video based on analysis of the constituent individuals within the
crowd and observing the movement of the crowd as a whole. Next,
according to one embodiment, business intelligence may be used to
decide placement and duration of dynamic advertisements. According
to at least one other embodiment, expert knowledge may be used to
decide administrative control and measures over the mass gathering
of people.
[0024] Referring to FIG. 1, an exemplary networked computer
environment 100 in accordance with one embodiment is depicted. The
networked computer environment 100 may include a computer 102 with
a processor 104 and a data storage device 106 that is enabled to
run a software program 108 and a crowd analysis program 110a. The
networked computer environment 100 may also include a server 112
that is enabled to run a crowd analysis program 110b that may
interact with a database 114 and a communication network 116. The
networked computer environment 100 may include a plurality of
computers 102 and servers 112, only one of which is shown. The
communication network 116 may include various types of
communication networks, such as a wide area network (WAN), local
area network (LAN), a telecommunication network, a wireless
network, a public switched network and/or a satellite network. It
should be appreciated that FIG. 1 provides only an illustration of
one implementation and does not imply any limitations with regard
to the environments in which different embodiments may be
implemented. Many modifications to the depicted environments may be
made based on design and implementation requirements.
[0025] The client computer 102 may communicate with the server
computer 112 via the communications network 116. The communications
network 116 may include connections, such as wire, wireless
communication links, or fiber optic cables. As will be discussed
with reference to FIG. 4, server computer 112 may include internal
components 902a and external components 904a, respectively, and
client computer 102 may include internal components 902b and
external components 904b, respectively. Server computer 112 may
also operate in a cloud computing service model, such as Software
as a Service (SaaS), Platform as a Service (PaaS), or
Infrastructure as a Service (IaaS). Server 112 may also be located
in a cloud computing deployment model, such as a private cloud,
community cloud, public cloud, or hybrid cloud. Client computer 102
may be, for example, a mobile device, a telephone, a personal
digital assistant, a netbook, a laptop computer, a tablet computer,
a desktop computer, an aerial drone flight computer, or any type of
computing devices capable of running a program, accessing a
network, and accessing a database 114. According to various
implementations of the present embodiment, the crowd analysis
program 110a, 110b may interact with a database 114 that may be
embedded in various storage devices, such as, but not limited to a
computer/mobile device 102, a networked server 112, or a cloud
storage service.
[0026] According to the present embodiment, a user using a client
computer 102 or a server computer 112 may use the crowd analysis
program 110a, 110b (respectively) to monitor crowds of people using
images taken from an aerial drone and analyze the images of the
crowd to determine risks or targeted advertisements in real-time.
The crowd analysis method is explained in more detail below with
respect to FIG. 3.
[0027] Referring now to FIG. 2, a networked aerial image capture
device system 200 according to at least one embodiment is depicted.
The networked aerial image capture device system 200 may include a
server 112 running the crowd analysis program 110b, a communication
network 116, and an aerial drone 202 (i.e., aerial image capturing
device). The aerial drone 202 may include a flight computer 204, a
wireless adapter 206, a still camera 208, and a video camera
210.
[0028] The crowd analysis program 110b running on the server 112
may communicate via the communication network 116 with the wireless
adapter 206 of the aerial drone 202. The communication network 116
may include wireless connections, such as wi-fi or satellite
communication. For example, the crowd analysis program 110b may
generate a flight path and transmit the generated flight path using
the communication network 116 to the wireless adapter 206 within
the aerial drone 202. The wireless adapter 206 may send and receive
data wirelessly using the communication network 116 and the
wireless adapter 206 may also interact with the flight computer
204.
[0029] The flight computer 204 may be a computer 102 designed to
control the aerial drone 202 in flight by keeping the aerial drone
202 level and flying the aerial drone 202 according to a flight
path. Furthermore, the flight computer 204 may control onboard
sensors, such as the still camera 208 and video camera 210 attached
to the bottom of the aerial drone 202. The flight computer 204 may
send the still camera 208 and the video camera 210 instructions to
move to point in a specific direction and when to capture images.
Images captured by the still camera 208 and the video camera 210
may be sent to the flight computer 204 for storage and
transmission. The flight computer 204 may then relay the captured
images to the wireless adapter 206. Then, the wireless adapter 206
may transmit the images using the communication network 116 to the
crowd analysis program 110b running on the server 112 for
analysis.
[0030] Referring now to FIG. 3, an operational flowchart
illustrating the exemplary crowd analysis process 300 used by the
crowd analysis program 110a and 110b according to at least one
embodiment is depicted.
[0031] At 302, an operational geographic area over a crowd of
gathered people is determined. The operational geographic area may
be determined based on input from a user, a warning from fixed
sensors near the geographic area, and so on. For example, a city
park may be designated as the operational geographic area by a
user.
[0032] Next, at 304, the geographic area is defined by a polygon.
Using known methods, the operational geographic area may be
represented as a polygon with a set of geographic coordinates. For
example, if the city park that was designated as the geographic
area is approximately rectangular in shape, then the geographic
coordinates of the four corner points may be determined and saved
as a set of geographic coordinates. From the saved geographic
coordinates, a polygon corresponding with the city park may be
defined that may be used as input into drone controlling
software.
[0033] Then, at 306, the polygon representing the geographic area
is partitioned into zones. The geographic area may be partitioned
into smaller geographic subdivisions or zones to efficiently
analyze the total operational geographic area. The partitioning may
include pure hexagonal or Cairo pentagonal zones and a set of
coordinates may be determined for the aerial drone 202 to be
positioned for each zone. Continuing the previous example, the city
park may be subdivided into four zones, such as zones Z.sub.1,
Z.sub.2, Z.sub.3, and Z.sub.4, with each zone having at least one
position coordinate associated with the zone.
[0034] At 308, a flight path over the zones is determined. Once the
geographic area is subdivided into zones as described previously at
306, a flight path may be determined for the aerial drone 202 to
fly over all of the zones, thus covering the original geographic
area. The aerial drone 202 may start from a point close to the area
of operation. A best route algorithm may be used to generate the
flight path for the aerial drone 202 to visit each position
coordinate within the set of position coordinates. For example, if
the area of operation for the aerial drone 202 is nearest Z.sub.3,
then, based on executing a best route algorithm, the determined
flight path may create a path from Z.sub.3 to Z.sub.2, from Z.sub.2
to Z.sub.4, and then from Z.sub.4 to Z.sub.1. More specifically,
the determined flight path may plot the flight path to the position
coordinate of Z.sub.3, then to the position coordinate for Z.sub.2,
then to the position coordinate for Z.sub.4, and finally to the
position coordinate for Z.sub.1. Furthermore, the height or
altitude of the aerial drone 202 may be set as part of the flight
path with differing altitudes at various points along the flight
path to circumvent obstacles or provide a more appropriate image
resolution given a zone size.
[0035] Next, at 310, the aerial drone 202 will fly according to the
determined flight path and capture images of the crowd of people.
As described previously, a server 112 running the crowd analysis
program 110b may wirelessly transmit the determined flight path to
the wireless adapter 206 of the aerial drone 202 using the
communication network 116. The aerial drone 202 may perform
self-controlled operation (i.e., autonomous flying) according to
the received flight path using the flight computer 204. As the
aerial drone 202 flies along the determined flight path, still
images may be captured at predefined time intervals using the still
camera 208 and video may be captured simultaneously using the video
camera 210. Additionally, the aerial drone 202 may loop through the
flight path continuously throughout the aerial drone's 202
operating time. As the aerial drone 202 flies to a position
coordinate, a predefined number of still images, such as two, may
be captured at a predefined time interval, such as two seconds
apart, in addition to capturing video footage before the drone
moves on to the next position coordinate in the flight path. The
aerial drone's 202 results may then be displayed in a dashboard or
other drone software.
[0036] Then, at 312, the captured images are partitioned. The
captured images may be transmitted wirelessly to a server 112 for
processing from the wireless adapter 206 of the aerial drone 202
using a communication network 116. At the server 112, the images
may be partitioned using a partitioning algorithm to create images
corresponding with each unique individual within the set of images
generated by the aerial drone 202. The partitioning algorithm may
identify geometry or other patterns consistent with the silhouette
of a person or by detecting features consistent with a face, or by
some other method. The image may then be partitioned by cropping or
delineating a region within an image to include a single individual
per partition. For example, a still image from zone Z.sub.2 may be
processed using a partitioning algorithm that identifies twenty
individuals. The partitioning algorithm would then create twenty
partitions from the still image with each partition including a
single individual. Image partitioning may be performed in a like
manner for each image of every zone until all images have been
similarly partitioned.
[0037] At 314, the characteristics of the individuals in the
partitioned images are predicted. Using a pre-trained machine
learning model, individual partitioned images may be analyzed to
determine characteristics of the person within each partitioned
image. Determined characteristics may include, for example,
demographics, such as age and gender. Additionally, the determined
characteristics may include other physical characteristics, such as
height, weight, and direction the individual is facing, and other
characteristics about an individual that may be derived from
analyzing an image. Furthermore, based on the location and
orientation of the aerial drone 202 when the picture was taken, the
geographic location of the area being photographed, and the
position of the individual within the image, the geographic
location of the individual may be determined. The determined
characteristics may then be saved as metadata. Thereafter, the
images may be appended with the metadata, including the
individual's age, gender, facing direction, location, and so on.
Alternatively, the metadata may be saved in a data structure, such
as an array, with a pointer or other indicator to an individual in
a partitioned image.
[0038] Next, at 316, the motion of the individuals in the images is
determined. By comparing the position of individuals from one
partitioned image to a second partitioned image of the same
individual, the movement direction and speed of each individual may
be tracked to determine an individual movement vector indicating
the movement direction and speed of the individual. For example, if
an individual appears in the center of a first image and the same
individual appears in the bottom-right portion in a second image,
then the individual has moved in a northeast direction based on the
known position and orientation of the aerial drone 202 and still
camera 208 when the images were generated. Alternatively, by
comparing the time the still images were taken to the same time in
the video footage, individuals may be identified within the video
since the still camera 208 and the video camera 210 may be mounted
in close proximity and therefore individuals may appear in similar
positions in the still images and the video generated at the same
time. Thus, using the video, an individual's movement may be
determined by tracking the change in the position of the individual
in the video footage.
[0039] Then, at 318, the aggregate motion, categorization, and
concentration of the crowd may be determined. Based on the movement
of the individuals within the crowd, the composite motion of the
crowd in general may also be determined. For example, by comparing
the movement of multiple individuals, a point of convergence may be
determined at a central location or a direction the crowd of people
may be moving towards that may be expressed as a crowd movement
vector. Furthermore, the crowd concentration may be determined
based on each individual's position within the defined geographic
area which may then be plotted as points. Based on the density of
the plotted points and movement of points corresponding to
individuals, the mass movement of the crowd may also be deduced by
the rate of entry and exit from a certain area of the zone.
Furthermore, based on the characteristics of the individuals
determined at 314, the characteristics of the collective crowd may
be determined to categorize the crowd. For example, if 88% of the
individuals are estimated to be within the ages of 16 to 25, the
crowd may be categorized as a crowd of a younger demographic of
people.
[0040] At 320, an action is taken in response to the information
determined about the crowd. Business intelligence may be applied to
the aggregate motion, categorization, and concentration of the
crowd. Based on the results from the applied business intelligence,
an advertiser may decide on a particular advertising strategy and
advertise to the crowd in real-time. Continuing the previous
example, if the crowd was categorized as a crowd of younger people,
specific advertising may be delivered to the individuals in the
crowd that is tailored for individuals within the crowd's age
demographic. The advertising may be electronically delivered to the
mobile devices of the individuals within the crowd, or by
contacting personnel amongst the crowd by radio, text message, and
so forth to distribute tangible advertising, such as flyers, that
may be most effective given the crowd characteristics.
[0041] Alternatively, the characteristics of the crowd, density,
and movement may raise security concerns and police or military may
respond to control the crowd, or move sensitive items or personnel
for protection or to reduce any hostilities. In security
applications, security intelligence may be applied to the aggregate
motion, categorization, and concentration of the crowd. The
resulting security intelligence analysis of the crowd information
may be used by security personnel to decide which security measures
should be taken and the extent, duration, and positioning of the
security measures. In another scenario, event organizers may use
the resulting crowd information to plan how to efficiently handle
the people within the crowd or how to make changes to minimize
future crowd formation and congestion.
[0042] It may be appreciated that FIGS. 2 and 3 provide only an
illustration of one embodiment and do not imply any limitations
with regard to how different embodiments may be implemented. Many
modifications to the depicted embodiment(s) may be made based on
design and implementation requirements.
[0043] FIG. 4 is a block diagram 900 of internal and external
components of computers depicted in FIG. 1 in accordance with an
illustrative embodiment of the present invention. It should be
appreciated that FIG. 4 provides only an illustration of one
implementation and does not imply any limitations with regard to
the environments in which different embodiments may be implemented.
Many modifications to the depicted environments may be made based
on design and implementation requirements.
[0044] Data processing system 902, 904 is representative of any
electronic device capable of executing machine-readable program
instructions. Data processing system 902, 904 may be representative
of a smart phone, a computer system, PDA, or other electronic
devices. Examples of computing systems, environments, and/or
configurations that may represented by data processing system 902,
904 include, but are not limited to, personal computer systems,
server computer systems, thin clients, thick clients, hand-held or
laptop devices, multiprocessor systems, microprocessor-based
systems, network PCs, minicomputer systems, and distributed cloud
computing environments that include any of the above systems or
devices.
[0045] User client computer 102 and network server 112 may include
respective sets of internal components 902 a, b and external
components 904 a, b illustrated in FIG. 4. Each of the sets of
internal components 902 a, b includes one or more processors 906,
one or more computer-readable RAMs 908, and one or more
computer-readable ROMs 910 on one or more buses 912, and one or
more operating systems 914 and one or more computer-readable
tangible storage devices 916. The one or more operating systems
914, the software program 108 and the crowd analysis program 110a
in client computer 102, and the crowd analysis program 110b in
network server 112, may be stored on one or more computer-readable
tangible storage devices 916 for execution by one or more
processors 906 via one or more RAMs 908 (which typically include
cache memory). In the embodiment illustrated in FIG. 4, each of the
computer-readable tangible storage devices 916 is a magnetic disk
storage device of an internal hard drive. Alternatively, each of
the computer-readable tangible storage devices 916 is a
semiconductor storage device such as ROM 910, EPROM, flash memory
or any other computer-readable tangible storage device that can
store a computer program and digital information.
[0046] Each set of internal components 902 a, b also includes a R/W
drive or interface 918 to read from and write to one or more
portable computer-readable tangible storage devices 920 such as a
CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical
disk or semiconductor storage device. A software program, such as
the software program 108 and the crowd analysis program 110a and
110b can be stored on one or more of the respective portable
computer-readable tangible storage devices 920, read via the
respective R/W drive or interface 918, and loaded into the
respective hard drive 916.
[0047] Each set of internal components 902 a, b may also include
network adapters (or switch port cards) or interfaces 922 such as a
TCP/IP adapter cards, wireless wi-fi interface cards, or 3G or 4G
wireless interface cards or other wired or wireless communication
links. The software program 108 and the crowd analysis program 110a
in client computer 102 and the crowd analysis program 110b in
network server computer 112 can be downloaded from an external
computer (e.g., server) via a network (for example, the Internet, a
local area network or other, wide area network) and respective
network adapters or interfaces 922. From the network adapters (or
switch port adaptors) or interfaces 922, the software program 108
and the crowd analysis program 110a in client computer 102 and the
crowd analysis program 110b in network server computer 112 are
loaded into the respective hard drive 916. The network may comprise
copper wires, optical fibers, wireless transmission, routers,
firewalls, switches, gateway computers and/or edge servers.
[0048] Each of the sets of external components 904 a, b can include
a computer display monitor 924, a keyboard 926, and a computer
mouse 928. External components 904 a, b can also include touch
screens, virtual keyboards, touch pads, pointing devices, and other
human interface devices. Each of the sets of internal components
902 a, b also includes device drivers 930 to interface to computer
display monitor 924, keyboard 926, and computer mouse 928. The
device drivers 930, R/W drive or interface 918, and network adapter
or interface 922 comprise hardware and software (stored in storage
device 916 and/or ROM 910).
[0049] It is understood in advance that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0050] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, network
bandwidth, servers, processing, memory, storage, applications,
virtual machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0051] Characteristics are as follows:
[0052] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0053] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0054] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0055] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0056] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported providing
transparency for both the provider and consumer of the utilized
service.
[0057] Service Models are as follows:
[0058] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0059] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0060] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0061] Deployment Models are as follows:
[0062] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0063] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0064] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0065] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0066] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
[0067] Referring now to FIG. 5, illustrative cloud computing
environment 1000 is depicted. As shown, cloud computing environment
1000 comprises one or more cloud computing nodes 100 with which
local computing devices used by cloud consumers, such as, for
example, personal digital assistant (PDA) or cellular telephone
1000A, desktop computer 1000B, laptop computer 1000C, and/or
automobile computer system 1000N may communicate. Nodes 100 may
communicate with one another. They may be grouped (not shown)
physically or virtually, in one or more networks, such as Private,
Community, Public, or Hybrid clouds as described hereinabove, or a
combination thereof. This allows cloud computing environment 1000
to offer infrastructure, platforms and/or software as services for
which a cloud consumer does not need to maintain resources on a
local computing device. It is understood that the types of
computing devices 1000A-N shown in FIG. 5 are intended to be
illustrative only and that computing nodes 100 and cloud computing
environment 1000 can communicate with any type of computerized
device over any type of network and/or network addressable
connection (e.g., using a web browser).
[0068] Referring now to FIG. 6, a set of functional abstraction
layers 1100 provided by cloud computing environment 1000 is shown.
It should be understood in advance that the components, layers, and
functions shown in FIG. 6 are intended to be illustrative only and
embodiments of the invention are not limited thereto. As depicted,
the following layers and corresponding functions are provided:
[0069] Hardware and software layer 1102 includes hardware and
software components. Examples of hardware components include:
mainframes 1104; RISC (Reduced Instruction Set Computer)
architecture based servers 1106; servers 1108; blade servers 1110;
storage devices 1112; and networks and networking components 1114.
In some embodiments, software components include network
application server software 1116 and database software 1118.
[0070] Virtualization layer 1120 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 1122; virtual storage 1124; virtual networks 1126,
including virtual private networks; virtual applications and
operating systems 1128; and virtual clients 1130.
[0071] In one example, management layer 1132 may provide the
functions described below. Resource provisioning 1134 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 1136 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may comprise application software
licenses. Security provides identity verification for cloud
consumers and tasks, as well as protection for data and other
resources. User portal 1138 provides access to the cloud computing
environment for consumers and system administrators. Service level
management 1140 provides cloud computing resource allocation and
management such that required service levels are met. Service Level
Agreement (SLA) planning and fulfillment 1142 provide
pre-arrangement for, and procurement of, cloud computing resources
for which a future requirement is anticipated in accordance with an
SLA.
[0072] Workloads layer 1144 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 1146; software development and
lifecycle management 1148; virtual classroom education delivery
1150; data analytics processing 1152; transaction processing 1154;
and crowd analysis 1156. A crowd analysis program 110a, 110b
provides a way to monitor crowds of people using images taken at an
altitude above the crowd from an image capturing device and analyze
the images of the crowd to determine risks or targeted
advertisements in real-time.
[0073] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
of the described embodiments. The terminology used herein was
chosen to best explain the principles of the embodiments, the
practical application or technical improvement over technologies
found in the marketplace, or to enable others of ordinary skill in
the art to understand the embodiments disclosed herein.
* * * * *