U.S. patent application number 10/698008 was filed with the patent office on 2004-07-22 for unified system and method for animal behavior characterization from top view using video analysis.
Invention is credited to Bai, Xuesheng, Kobla, Vikrant, Liang, Yiqing, Zhang, Yl.
Application Number | 20040141635 10/698008 |
Document ID | / |
Family ID | 24885865 |
Filed Date | 2004-07-22 |
United States Patent
Application |
20040141635 |
Kind Code |
A1 |
Liang, Yiqing ; et
al. |
July 22, 2004 |
Unified system and method for animal behavior characterization from
top view using video analysis
Abstract
In general, the present invention is directed to systems and
methods for finding the position and shape of an animal using
video. The invention includes a system with a video camera coupled
to a computer in which the computer is configured to automatically
provide animal segmentation and identification, animal motion
tracking (for moving animals), animal feature points and segments
identification, and behavior identification. In a preferred
embodiment, the present invention may use background subtraction
for animal identification and tracking, and a combination of
decision tree classification and rule-based classification for
feature points and segments and behavior identification. Thus, the
present invention is capable of automatically monitoring a video
image to identify, track and classify the actions of various
animals and the animal's movements within the image. The image may
be provided in real time or from storage. The invention is
particularly useful for monitoring and classifying animal behavior
for testing drugs and genetic mutations, but may be used in any of
a number of other surveillance applications.
Inventors: |
Liang, Yiqing; (Vienna,
VA) ; Bai, Xuesheng; (Reston, VA) ; Kobla,
Vikrant; (Ashburn, VA) ; Zhang, Yl; (Fairfax,
VA) |
Correspondence
Address: |
Yiqing Liang
1334 Stokley Way
Vienna
VA
22182
US
|
Family ID: |
24885865 |
Appl. No.: |
10/698008 |
Filed: |
October 30, 2003 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10698008 |
Oct 30, 2003 |
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09718374 |
Nov 24, 2000 |
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6678413 |
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Current U.S.
Class: |
382/110 ;
382/181 |
Current CPC
Class: |
G06V 40/103 20220101;
A61B 2503/42 20130101; G06V 40/23 20220101; G06T 7/20 20130101;
A61B 5/1116 20130101; A61B 5/1118 20130101; A61B 5/1113 20130101;
A01K 29/005 20130101; G06T 2207/30004 20130101; G06V 40/20
20220101; A61B 5/7267 20130101; A61B 5/1128 20130101; G16H 40/67
20180101; A61B 2503/40 20130101; A61B 5/4094 20130101; A61B 5/7264
20130101; A01K 1/031 20130101 |
Class at
Publication: |
382/110 ;
382/181 |
International
Class: |
G06K 009/00 |
Goverment Interests
[0001] Portions of the material in this specification arose as a
result of Government support under grants MH58964 and MH58964-02
between Clever Sys., Inc. and The National Institute of Mental
Health, National Institute of Health. The Government has certain
rights in this invention.
Claims
What is claimed is:
1. A video-based animal behavior analysis system, comprising: a
computer configured to determine a position and shape of an animal
from video images and characterize activity of said animal based on
analysis of changes in said position and said shape over time.
2. The system of claim 1, further comprising: a video camera and a
video digitization unit coupled to said computer for capturing said
video images and converting said video images from analog to
digital format.
3. The system of claim 2, further comprising: an animal
identification, segregation, and tracking module receiving said
video images.
4. The system of claim 3, wherein said computer further includes a
behavior identification module for characterizing activity of said
animal, said behavior identification module being coupled to said
animal identification, segregation, and tracking module.
5. The system of claim 4, wherein said computer further includes a
standard animal behavior storage module that stores information
about known behavior of a predetermined standard animal for
comparing the activity of said animal, said standard animal
behavior storage module being coupled to said behavior
identification module.
6. The system of claim 1, wherein said animal is a mouse.
7. The system of claim 1, wherein said animal is a rat.
8. A method of determining and characterizing activity of an animal
using computer processing of video images, comprising the steps of:
detecting an animal in said video images; tracking changes to said
animal over a plurality of said video images; identifying and
classifying said changes to said animal; and characterizing said
activity of said animal based on comparison to pre-trained models
or rules of such activity or based on calculation of behavioral
parameters of behavioral processes and behavioral events.
9. The method of claim 8, wherein said detecting an animal includes
using a background subtraction method comprising the steps of:
apply a adaptive or constant threshold on the difference values
between a current image and a background so as to determine a broad
region of interest; post-process the various pixels in said region
of interest to obtain said animal using various morphological and
area refinement techniques; and refine contours of said animal
image by smoothing.
10. The method of claim 8, wherein said step of identifying and
classifying changes to said animal includes using statistical shape
information selected from the group consisting of: area of the
animal; centroid position of the animal; bounding box and its
aspect ratio of the animal; eccentricity of the animal; and a
directional orientation of the animal relative to an axis as
generated with a Principal Component Analysis.
11. The method of claim 8, wherein said steps are also performed in
night conditions by using red light to simulate such night
conditions, or by using infra-red cameras to capture the images
with no light;
12. The method of claim 8, wherein said steps are also performed
with a plurality of cages or arenas, each of which contains a
single animal;
13. The method of claim 8, wherein said step of characterizing said
activity of said animal based on calculation of behavior parameters
of behavioral processes and behavioral events includes the steps
of: locating feature points and segments of the said animal;
detecting behavior events by comparing animal feature against
predefined rules; and detecting behavior parameters of behavioral
processes.
14. The method of claim 13, wherein said step of locating feature
points and segments of the said animal includes the step of
detecting body parts of the animal;
15. The method of claim 14, wherein said body parts include the
head;
16. The method of claim 14, wherein said body parts include the
tail;
17. The method of claim 14, wherein said body parts include the
waist;
18. The method of claim 14, wherein said body parts include the
fore body;
19. The method of claim 14, wherein said body parts include the
hind body;
20. The method of claim 8, wherein said video images include images
captured of various animal behavioral analysis apparatuses and said
tracking, identifying and characterizing of activities is performed
on those animal behavioral analysis apparatuses.
21. The method of claim 20, wherein said various animal behavioral
analysis apparatuses include home cage, a cage looking like a
shoebox used for housing animals.
22. The method of claim 20, wherein said various animal behavioral
analysis apparatuses include open field, in various shapes such as
circular, square, or rectangular.
23. The method of claim 20, wherein said various animal behavioral
analysis apparatuses include Water Maze, made of a circular pool
filled with water and a hidden clear or white Plexiglas
platform.
24. The method of claim 20, wherein said various animal behavioral
analysis apparatuses include Y-maze (three-sided runway, where one
arm can deliver electrical foot-shock through its floor grid),
T-maze (Runways are in the shape of T; its sides are made of black
Plexiglas or wood; its floor is metal mesh.), and Radial arm maze
(comprised of 8 or 12 arms, radiating from a central start box,
made of Plexiglas or wood).
25. The method of claim 20, wherein said various animal behavioral
analysis apparatuses include zero maze, made of brightly lit, open
areas alternating with dark, covered areas, comprising the annulus
of an elevated circular runway.
26. The method of claim 20, wherein said various animal behavioral
analysis apparatuses include Elevated Plus maze, comprising of four
narrow runways, two well lit and open, and two alternating enclosed
with walls and dark, and a center box where the animal is placed
initially.
27. The method of claim 20, wherein said various animal behavioral
analysis apparatuses include Object Recognition, where multiple
objects of different shapes and colors are placed in an open
field.
28. The method of claim 20, wherein said various animal behavioral
analysis apparatuses include cued or conditioned fear chambers used
for freezing
29. The method of claim 20, wherein said various animal behavioral
analysis apparatuses includes using an unified framework, called
"virtual apparatus", which uses a graphic tools to simulate various
types of apparatuses.
30. The method of claim 20, wherein said various animal behavioral
analysis apparatuses includes "virtual zones", which are created
with graphic tools provided in the system to simulate various types
of dividing zones within the apparatuses.
31. The method of claim 13, wherein said detection of behavioral
events includes turning ratio: ratio of path length traveled over
number of turns, where number of turns is counted when the animal
makes a turn larger than 90 degrees when the animal travels one
body length.
32. The method of claim 13, wherein said detection of behavioral
events includes sniffing at objects, an event counted when animal's
nose is in contact with an object in a object recognition
apparatus
33. The method of claim 13, wherein said detection of behavioral
events includes stretch-and-attend: Cautious approach with fore
body stretched and lowered followed by the retraction of the fore
body.
34. The method of claim 13, wherein said detection of behavioral
events includes stay-across-areas: partial incursions into
particular zones. For example, the animal might maintain its hind
quarters in a closed arm while poking its nose into an open
arm.
35. The method of claim 13, wherein said detection of behavioral
events includes head dipping, exploratory movement of
head/shoulders over the side of the maze.
36. The method of claim 13, wherein said detection of behavior
events includes the behavior of freezing, and said freezing
behavior is determined by the absence of movement of rodent body
for a brief period of time;
37. The method of claim 13, wherein said detection of behavior
events includes the behavior of locomoting, and said locomotion
behavior is determined by the movement of the rodent around the
cage or arena when viewed from the top;
38. The method of claim 13, wherein said detection of behavior
events includes the behavior of transgressing from zone to another,
and said transgression behavior is detected by the movement of a
portion of, or the entire body of the rodent across from one
defined zone or area into another defined zone or area;
39. The method of claim 13, wherein said detection of behavior
parameters of behavioral processes includes proximity score:
calculated by determining the distance of the animal from the goal
during each second of the trial and is used as a measure of
deviation from the ideal path to the platform once an animal is
placed in a water maze setting.
40. The method of claim 13, wherein said detection of behavior
parameters of behavioral processes includes heading errors: defined
as an instance of swimming away from the VISIBLE platform in a
water maze setting.
41. The method of claim 13, wherein said calculation of behavior
parameters of behavioral processes includes: instant and average
speed of movements, distance traveled, its instant and cumulative
body turning angles.
Description
BACKGROUND OF THE INVENTION
[0002] 1. Technical Field
[0003] The invention relates generally to behavior analysis of
animal objects. More particularly, one aspect of the invention is
directed to monitoring and characterization of behaviors under
specific behavioral paradigm experiments, including home cage
behavior paradigms, locomotion or open field paradigm experiment,
object recognition paradigm experiments, variety of maze paradigm
experiments, water maze paradigm experiments, freezing paradigm
experiments for conditioned fear, for an animal, for example, a
mouse or a rat, using video analysis from a top view image or side
view image, or the integration of both views.
[0004] 2. Background Art
[0005] Animals, for example mice or rats, are used extensively as
human models in the research of drug development; genetic
functions; toxicology research; understanding and treatment of
diseases; and other research applications. Despite the differing
lifestyles of humans and animals, for example mice, their extensive
genetic and neuroanatomical homologies give rise to a wide variety
of behavioral processes that are widely conserved between species.
Exploration of these shared brain functions will shed light on
fundamental elements of human behavioral regulation. Therefore,
many behavioral test experiments have been designed on animals like
mice and rats to explore their behaviors. These experiments
include, but not limited to, home cage behaviors, open field
locomotion experiments, object recognition experiments, a variety
of maze experiments, water maze experiments, and freezing
experiments for conditioned fear.
[0006] Animal's home cage activity patterns are important
examination item on the general health list of animals, such as
mice and rats. It provides many important indications of whether
the animal's health status is normal or abnormal. Home cage
behaviors are best observed by videotaping several 24-hour periods
in the animal housing facility, and subsequent scoring of the
videotape by two independent observers. However, this observation
has rarely been done until our inventions came into play, due to
the instability in long term human observation, the time consumed,
and the huge costs associated with the observation.
[0007] A conventional method for measuring animal's spatial
navigation learning and memory is the water maze task, in which the
animal swims to find a hidden platform, using visual cues to locate
the platform. This task is based on the principle that rodents are
highly motivated to escape from a water environment by the
quickest, most direct route. (Wenk, 1997) Experiment sessions are
usually videotaped, and human observation of videotapes or
automated software is used, depending on the parameters required
for observing the session. Variations of popular methods for
measuring animal's spatial navigation learning and memory include
other maze designs, such as T-maze, Y-maze, and radial arm maze. In
all cases, the task requires the animal to choose specific arm(s)
of the maze to receive a food or water reinforcement or to avoid a
footshock. The shapes of the arms make the differences among
T-maze, Y-maze, and radial arm maze. The animal is habituated and
then shaped to obtain the reinforcer. Variations of popular methods
for measuring animal's anxiety-related behaviors include elevated
plus maze, zero maze, etc. Measuring anxiety using elevated plus
maze or zero maze rests on the naturalistic conflict between the
tendency of animal such as mice to explore a novel environment and
the aversive properties of a brightly lit, open area. Elevated plus
maze is elevated from the ground about a meter with four (4) arms,
two well lit and two closed and dark. Animals such as mice or rats
prefer the closed arms but will venture out into the open arms,
with a start box in the center. The zero maze is similar, but has
annulus of an elevated circular runway, with areas brightly lit
alternate with dark, covered areas. We group all of these
experiments under "maze". Though the theory and operations and
training may be different among these mazes, the observation and
measurement is basically similar, i.e., the measurement of animal
staying in each arm or arena, closed or open. Experiment session is
usually videotaped, and human observation of videotapes or
automates software is used, depending on the parameters required
for observing the session.
[0008] Another method to measure animal's such as mice or rats,
capability in spatial learning and memory and their tendency of
exploration is the experiment of object recognition, or novelty
seeking. Its objective is to measure reduced time spent exploring a
novel object that replaced a training object after a specified
retention time. Objects of different shapes and colors are placed
in an open field, and animal is place in the field. The number of
times the animal sniffs at each object, and the duration of each
sniffing are measured to show the animal's tendency to explore.
Objects are replaced with new objects from time to time. The
experiment session is videotaped, and human observation of
videotapes is used to measure those parameters.
[0009] The most standardized general measure of motor function is
spontaneous activity in the open field. Square, rectangular, and
circular equipment is presently in common use. Sizes of open fields
range from centimeters to several meters. Scoring of videotaped
session allows quantization of animal's spontaneous activity.
Automated open fields now routinely used in behavioral neuroscience
laboratories are equipped with either photocell beams or video
tracking and computer software. Both types of automated systems
calculate a useful range of basic locomotor parameters.
[0010] Freezing test is designed for cued and contextual fear
conditioning, which is among the most intuitive memory paradigms.
Freezing is a common response feared situation in many species, and
is defined as no movements other than respiration. Conditioning
training consists of placing the mouse in the chamber and exposing
to a mild footshock paired with auditory cue. Freezing is measured
when the trained mouse is placed back in the same chamber for
training with auditory cue, and scored for bouts of freezing
behavior. Human observation of videotapes of the session is used
for this scoring, which is inaccurate and expensive. Automated
system using mechanical principle exists to help real-time scoring
of freezing. However, the precision of such a mechanical system
need to be further improved.
[0011] As discussed, all these apparatus and experiments use, in
many cases, human observation of videotapes of the experiment
sessions, resulting in inaccuracy, subjectivity, labor-intensive,
and thus expensive experiments. Some automating software provides
rudimentary and basic parameters, relying on tracking animal as a
point in space, generating experiment results that are inaccurate
and can not meet the demands for advanced features. Besides, each
system software module works for only a specific experiment,
resulting in potential discrepancy in the results across different
systems due to differences in software algorithms used.
[0012] All the observations of these behavioral experiments use
video to record experiment processes and rely human observations.
This introduces the opportunity to utilize the latest technologies
development in computer vision, image processing, and digital video
processing to automate the processes and achieve better results,
high throughput screening, and lower costs. Many of these
experiments are conducted with observations performed from top
view, that is, observation of the experiments from above the
apparatus is used to obtain needed parameters. This also provides
an opportunity to unify the approaches to observe and analyze these
experiments' results.
SUMMARY OF THE INVENTION
[0013] There are strong needs for automated systems and software
that can automate the measurements of the experiments mentioned
above, provide the measurements of meaningful complex behaviors and
revealing new parameters that characterize animal behaviors to meet
post-genomic era's demands, and obtain consistent results using
novel approaches.
[0014] A revolutionary approach is invented to automatically
measure animal's home cage activity patterns. This approach
consists of defining a unique set of animal's, such as mice or
rats, behavior category. This category includes behaviors like
rearing, walking, grooming, eating, drinking, jumping, hanging,
etc. Computer systems are designed and implemented that can product
digital video files of animal's behaviors in a home cage in real
time or off-line mode. Software algorithms are developed to
automatically understand the animal's behaviors in those video
files.
[0015] A novel and unified framework is provided for automatically
analyzing animal behaviors from multiple different behavioral
paradigms. This unified framework lays the foundation and
constitutes the common layer for all the automated measurements in
the experiments of water maze, maze, locomotion, object
recognition, and freezing for fear conditioning.
[0016] Creative algorithms are designed to support innovative
analysis of behaviors of animals such as mice or rats. This
analysis is based on the premise that the entire animal body, body
parts, related color information, and their dynamic motion are
taken advantage of in order to provide the measurement of complex
behaviors and novel parameters.
[0017] Virtual apparatus is designed and implemented in the system
to ensure same software framework can be applied to different
apparatuses such as water maze, variations of mazes including
T-maze, Y-maze, radial arm maze, elevated plus maze, zero maze,
cages of rectangular, circular, or any other shapes, and object of
any color and shapes, instead of having different software
component to handle different apparatus,. The software is designed
to provide graphic tools, and users can use these graphic tools to
create virtual apparatus corresponding to the real apparatus that
is being used for the experiment under observation. Graphic tools
also provide the capability to calibrate these apparatus, allowing
better identification of behaviors and precise measurement of
behaviors in real measurements instead of relative measurement
using number of pixels.
[0018] Virtual zones are another invention that has been
implemented in the system. In all the experiments, the animal is
moving around the cage, and the activity distribution in different
zones of the cage is of great interest to many scientists.
Convention approach is to use infrared photobeams to divide the
cage into zones needed. Photobeams are released from tubes at one
end, and sensor on the opposite end receives the photobeams. When
photobeams are interrupted by animal, the receiver records the
signal. In this way, the animal can move around while their
activity distribution across zones is recorded and calculated. In
this invention, virtual zones are used in stead of the zones
created by photobeams. Graphic tools are designed and provided for
the users. Users draw the zones as they want using the graphic
tools. Software tracks the animals and records how the animal
crosses the zones and stay in the zones as defined by the
drawings.
[0019] Another invention is that algorithms are designed and
implemented to allow different modules to be combined to achieve
multiple experiment purposes in a joint operation. For one example,
measuring animal's object recognition behaviors and measuring
animal's locomotion activity can be performed during one experiment
operation. The users can perform their regular object recognition
experiment. However, software modules of object recognition and
locomotion analysis are both executed to obtain analysis results of
both. This flexibility enhances the power and usability of the
system.
[0020] In general, the present invention is directed to systems and
methods for finding patterns of behaviors and/or activities of an
animal using video. The invention includes a system with a video
camera connected to a computer in which the computer is configured
to automatically provide animal identification, animal motion
tracking (for moving animal), animal shape, animal body parts, and
posture classification, and behavior identification. Thus, the
present invention is capable of automatically monitoring a video
image to identify, track and classify the actions of various
animals and their movements. The video image may be provided in
real time from a camera and/or from a storage location. The
invention is particularly useful for monitoring and classifying
mice or rats behavior for testing drugs and genetic mutations, but
may be used in any of a number of surveillance or other
applications.
[0021] In one embodiment the invention includes a system in which
an analog/digital video camera and a video record/playback device
(e.g., VCR) are coupled to a video digitization/compression unit.
The video camera may provide a video image containing an animal to
be identified. The video digitization/compression unit is coupled
to a computer that is configured to automatically monitor the video
image to identify, track and classify the actions of the animal and
its movements over time within a sequence of video session image
frames. The digitization/compression unit may convert analog video
and audio into, for example, MPEG or other formats. The computer
may be, for example, a personal computer, using either a Windows
platform or a Unix platform, or a MacIntosh computer and compatible
platform. The computer is loaded and configured with custom
software programs (or equipped with firmware) using, for example,
MATLAB or C/C++ programming language, so as to analyze the
digitized video for animal identification and segmentation,
tracking, and/or behavior/activity characterization. This software
may be stored in, for example, a program memory, which may include
ROM, RAM, CD ROM and/or a hard drive, etc. In one variation of the
invention the software (or firmware) includes a unique background
subtraction method which is more simple, efficient, and accurate
than those previously known.
[0022] In operation, the system receives incoming video images from
either the video camera in real time or pre-recorded from the video
record/playback unit. If the video is in analog format, then the
information is converted from analog to digital format and may be
compressed by the video digitization/compression unit. The digital
video images are then provided to the computer where various
processes are undertaken to identify and segment a predetermined
animal from the image. In a preferred embodiment the animal is a
mouse or rat in motion with some movement from frame to frame in
the video, and is in the foreground of the video images. In any
case, the digital images may be processed to identify and segregate
a desired (predetermined) animal from the various frames of
incoming video. This process may be achieved using, for example,
background subtraction, mixture modeling, robust estimation, and/or
other processes.
[0023] The shape and location of the desired animal is then tracked
from one frame or scene to another frame or scene of video images.
The body parts of the animal such as head, mouth, and tail are
identified by novel approaches through body contour segmentation,
contour segment classification, and relaxation labeling. Next, the
changes in the shapes, locations, body parts, and/or postures of
the animal of interest may be identified, their features extracted.
Then, the shape, location, body parts, and other related
information may be used to characterize the animal's activity into
one of a number of pre-defined behaviors. For example, if the
animal is a mouse or rat, some pre-defined normal behaviors may
include sleeping, walking, sniffing, etc., and pre-defined abnormal
behavior may include spinning vertical, jumping in the same spot,
etc. The pre-defined behaviors may be stored in a database in the
data memory. The behavior may be characterized using, for example,
approaches such as rule-based analysis, token parsing procedure,
and/or Hidden Markov Modeling (HMM). Further, the system may be
constructed to characterize the object behavior as new behavior and
particular temporal rhythm.
[0024] In another preferred embodiment directed towards the video
camera providing a video image containing animals such as mice or
rats to be identified, the system operates as follows. There is at
least one camera, or multiple cameras, taking video image of
experiment apparatus that contain animals. There is at least one
apparatus, or as many as the computer computing power allows, say
four (4) or sixteen (16) or even more. Each apparatus contains at
least one animal or multiple animals. The multiple cameras may be
taking video from different points of views such as one taking
video images from the side of the apparatus, or one taking video
images from the top of the apparatus. These apparatus can be home
cage, open field cage, water maze device, T-maze device, Y-maze
device, radial arm device, zero maze device, elevated plus maze
device, or other experiment devices. When video images are taken of
multiple apparatuses and devices containing one or multiple
animals, and are analyzed for identifying these animals' behaviors,
high throughput screening is achieved. When video images taken from
different points of views, for example, one from the top view and
another from the side view, are combined to identify animal's
behaviors, integrated analysis is achieved.
[0025] In another preferred embodiment directed toward video
analysis of animals such as mice or rats, the system operates as
follows. As a preliminary matter, normal postures and behaviors of
the animals are defined and may be entered into a Normal Paradigm
Parameters, Postures and Behaviors database. In analyzing, in a
first instant, incoming video images are received. The system
determines if the video images are in analog or digital format and
input into a computer. If the video images are in analog format
they are digitized and may be compressed, using, for example, an
MPEG digitizer/compression unit. Otherwise, the digital video image
may be input directly to the computer. Next, a background may be
generated or updated from the digital video images and foreground
objects detected. Next, the foreground animal features are
extracted. Also, body parts such as head, tail, ear, mouth,
forelimbs, hind limbs, abdomen, and upper and lower back, are
identified. Two different methods are pursuing from this point,
depending on different behavior paradigms. In one method, the
foreground animal shape is classified into various categories, for
example, standing, sitting, etc. Next, the foreground animal
posture is compared to the various predefined postures stored in
the database, and then identified as a particular posture or a new
(unidentified) posture. Then, various groups of postures and body
parts are concatenated into a series to make up a foreground animal
behavior compared against the sequence of postures, stored in for
example a database in memory, that make up known normal or abnormal
behaviors of the animal. The abnormal behaviors are then identified
in terms of known abnormal behavior, new behavior and/or daily
rhythm. In another method, behavioral processes and events are
detected, and behavior parameters are calculated. These behaviors
parameters give indications to animal health information related to
learning and memory capability, anxiety, and relations to certain
diseases.
[0026] In one variation of the invention, animal detection is
performed through a unique method of background subtraction. First,
the incoming digital video signal is split into individual images
(frames) in real-time. Then, the system determines if the
background image derived from prior incoming video needs to be
updated due to changes in the background image or a background
image needs to be developed because there was no background image
was previously developed. If the background image needs to be
generated, then a number of frames of video image, for example 20,
will be grouped into a sample of images. Then, the system creates a
standard deviation map of the sample of images. Next, the process
removes a bounding box area in each frame or image where the
variation within the group of images is above a predetermined
threshold (i.e., where the object of interest or moving objects are
located). Then, the various images within the sample less the
bounding box area are averaged. Final background is obtained by
averaging 5-10 samples. This completes the background generation
process. However, often the background image does not remain
constant for a great length of time due to various reasons. Thus,
the background needs to be dynamically recalculated periodically as
above or it can be recalculated by keeping track of the difference
image and note any sudden changes. The newly dynamically generated
background image is next subtracted from the current video image(s)
to obtain foreground areas that may include the object of
interest.
[0027] Next, the object identification/detection process is
performed. First, regions of interest (ROI) are obtained by
identifying areas where the intensity difference generated from the
subtraction is greater than a predetermined threshold, which
constitute potential foreground object(s) being sought.
Classification of these foreground regions of interest will be
performed using the sizes of the ROIs, distances among these ROIs,
threshold of intensity, and connectedness, to thereby identify the
foreground objects. Next, the foreground object
identification/detection process may be refined by adaptively
learning histograms of foreground ROIs and using edge detection to
more accurately identify the desired object(s). Finally, the
information identifying the desired foreground object is output.
The process may then continue with the tracking and/or behavior
characterization step(s).
[0028] The previous embodiments are particularly applicable to the
study and analysis of mice or rats used in genetic and drug
experimentation. Another variation of the present invention is
directed to automatically determining locomotion behavior of mice
or rats in an open field. Once the animal is identified as
foreground object as discussed above, the body parts of the animal
such as head and tail, and hind limbs and forelimbs, and center of
mass, are identified. The traces of the path of the movements of
the animal's center of mass in the open field under observation is
recorded, its instant and average speed of movements and distance
traveled are calculated, its instant and cumulative body turning
angles are analyzed. In addition, events like turning ratio (ratio
of path length over number of turns, where number of turns is
counted when the animal makes a turn larger than 80 degrees when
the animal travels one body length); proximity score (calculated by
determining the distance of the animal from the goal during each
second of the trial and is used as a measure of deviation from the
ideal path to the platform once an animal is placed in the cage);
heading errors (defined as an instance of swimming away from the
VISIBLE platform); and animal staying in a specific zone inside the
field, are recorded. Then a visualization process will further
analyze the result of the path trace recorded to generate variety
of statistic results. Visualization process allows users to use
graphic drawing tools to define any number of zones of any shape in
the open field as needed. The system provides a graphic tool that
allows users to define the field of any shape.
[0029] The previous embodiments are particularly applicable to the
study and analysis of mice or rats in their capability to explore
new objects. Another variation of the present invention is directed
to automatically determining the object recognition activity.
Graphic tools are provided to allow users to define objects in the
scene. Once the animal is identified as a foreground object as
discussed above, the body parts of the animal such as head and tail
and hind limbs and forelimbs, and center of mass are identified.
The traces of the path of the movements of the animal's center of
mass are recorded. The distances of the animal's head to any of the
objects in the scene are calculated and when the distance to an
object is less than a user-defined amount, the animal is counted as
animal's sniffing at the object and is said to be exploring that
object. Statistics about these exploring events are generated and
exported.
[0030] The previous embodiments are particularly applicable to the
study and analysis of mice or rats in their spatial learning and
memory. Third variation of the present invention is directed to
automatically determining the behaviors of mice or rats in a water
maze experiment environment. Graphic tools are provided to allow
users to define the maze and platforms. Once the animal is
identified as a foreground object as discussed above, the body
parts of the animal such as head and tail and hind limbs and
forelimbs, and center of mass are identified. The traces of the
path of the movements of the animal's center of mass are recorded.
The latency (the time period the animal spent in swimming in the
water before landing at the platform) is measured; its instant and
average speed of movements and distance traveled are calculated;
its instant and cumulative body turning angles are analyzed. In
addition, events like turning ratio (ratio of path length over
number of turns, where number of turns is counted when the animal
makes a turn larger than 90 degrees when the animal travels one
body length); proximity score (calculated by determining the
distance of the animal from the goal (platform) during each second
of the trial and is used as a measure of deviation from the ideal
path to the platform once an animal is placed in the water);
heading errors (defined as an instance of swimming away from the
VISIBLE platform); and animal staying in a specific zone inside the
maze, are recorded. Then a visualization process will further
analyze the result of the path trace recorded to generate variety
of statistic results. Visualization process allows users to use
graphic drawing tools to define any number of zones of any shape in
the open field as needed. The system provides a graphic tool that
allows users to define the field of any shape.
[0031] The previous embodiments are particularly applicable to the
study and analysis of mice or rats in their spatial learning and
memory and anxiety. Fourth variation of the present invention is
directed to automatically determining the behaviors of mice or rats
in a variety of maze apparatus. Graphic tools are provided to
define specific maze apparatus, such as T-maze, Y-maze, radial arm
maze, zero maze, elevated plus maze, and etc. Once the animal is
identified as a foreground object as discussed above, the body
parts of the animal such as head and tail and hind limbs and
forelimbs, and center of mass are identified. The traces of the
path of the movements of the animal's center of mass are recorded.
More importantly, the animal's behaviors related to every arm in
the maze, such as time spent in each arm, the number of times
entering and exiting an arm, are found. Besides, anima's instant
and average speed of movements and distance traveled are
calculated; its instant and cumulative body turning angles are
analyzed. In addition, events such as animal partial incursions
into particular arm (for example, the animal might maintain its
hind quarters in a closed arm while poking its nose into an open
arm); Stretch-Attend Behavior; Head-Dipping behavior; and Supported
Rearing, are detected.
[0032] Fifth variation of the present invention is directed to
automatically determining the freezing behaviors of mice or rats in
a cued or conditioned fear tests. Graphic tools are provided.
Graphic tools are provided to define the area within which animal
activity is measured. Differences between neighboring frames are
compared pixel-by-pixel in terms of their intensity and color
intensity. These differences are used to calculate the motion of
the animal from frame-to-frame because motion in the area is caused
by movements of the animal. The values of these differences
indicate if the animal is moving or freezing.
[0033] Development activities have been completed to validate
various scientific definitions of mouse behaviors and to create
novel digital video processing algorithms for mouse tracking and
behavior recognition, which are embodied in a software and hardware
system according to the present invention. An automated method for
analysis of mouse behavior from digitized 24 hours video has been
achieved using the present invention and its digital video analysis
method for object identification and segmentation, tracking, and
classification. Several different methods and their algorithms,
including Background Subtraction, Probabilistic approach with
Expectation-Maximization, and Robust Estimation to find parameter
values by best fitting a set of data measurements and results
proved successful.
[0034] The previous embodiments are particularly applicable to the
study and analysis of mice or rats used in genetic and drug
experimentation. One variation of the present invention is directed
particularly to automatically determining the behavioral
characteristics of a mouse in a home cage, a cage looking like a
shoebox used for housing animals. The need for sensitive detection
of novel phenotypes of genetically manipulated or drug-administered
mice demands automation of analyses. Behavioral phenotypes are
often best detected when mice are unconstrained by experimenter
manipulation. Thus, automation of analysis of behavior in a known
environment, for example a home cage, would be a powerful tool for
detecting phenotypes resulting from gene manipulations or drug
administrations. Automation of analysis would allow quantification
of all behaviors as they vary across the daily cycle of activity.
Because gene defects causing developmental disorders in humans
usually result in changes in the daily rhythm of behavior, analysis
of organized patterns of behavior across the day may also be
effective in detecting phenotypes in transgenic and targeted mutant
mice. The automated system may also be able to detect behaviors
that do not normally occur and present the investigator with video
clips of such behavior without the investigator having to view an
entire day or long period of mouse activity to manually identify
the desired behavior.
[0035] The systematically developed definition of mouse behavior
that is detectable by the automated analysis according to the
present invention makes precise and quantitative analysis of the
entire mouse behavior repertoire possible for the first time. The
various computer algorithms included in the invention for
automating behavior analysis based on the behavior definitions
ensure accurate and efficient identification of mouse behaviors. In
addition, the digital video analysis techniques of the present
invention improves analysis of behavior by leading to: (1)
decreased variance due to non-disturbed observation of the animal;
(2) increased experiment sensitivity due to the greater number of
behaviors sampled over a much longer time span than ever before
possible; and (3) the potential to be applied to all common
normative behavior patterns, capability to assess subtle behavioral
states, and detection of changes of behavior patterns in addition
to individual behaviors.
[0036] The invention may identify some abnormal behavior by using
video image information (for example, stored in memory) of known
abnormal animals to build a video profile for that behavior. For
example, video image of vertical spinning while hanging from the
cage top was stored to memory and used to automatically identify
such activity in mice. Further, abnormalities may also result from
an increase in any particular type of normal behavior. Detection of
such new abnormal behaviors may be achieved by the present
invention detecting, for example, segments of behavior that do not
fit the standard profile. The standard profile may be developed for
a particular strain of mouse whereas detection of abnormal amounts
of a normal behavior can be detected by comparison to the
statistical properties of the standard profile.
[0037] Thus, the automated analysis of the present invention may be
used to build profiles of the behaviors, their amount, duration,
and daily cycle for each animal, for example each commonly used
strain of mice. A plurality of such profiles may be stored in, for
example, a database in a data memory of the computer. One or more
of these profiles may then be compared to a mouse in question and
difference from the profile expressed quantitatively.
[0038] The techniques developed with the present invention for
automation of the categorization and quantification of all
home-cage mouse behaviors throughout the daily cycle is a powerful
tool for detecting phenotypic effects of gene manipulations in
mice. As previously discussed, this technology is extendable to
other behavior studies of animals and humans, as well as
surveillance purposes. As will be described in detail below, the
present invention provides automated systems and methods for
automated accurate identification, tracking and behavior
categorization of an object whose image is captured with video.
[0039] Other variations of the present invention is directed
particularly to automatically determining the behavioral
characteristics of an animal in various behavioral experiment
apparatus such as water maze, Y-maze, T-maze, zero maze, elevated
plus maze, locomotion open field, field for object recognition
study, and cued or conditioned fear. In these experiment
apparatuses, animal's body contour, center of mass, body parts
including head, tail, forelimbs, hind limbs and etc. are accurately
identified using the embodiments above. This allows excellent
understanding of animal's behaviors within these specific
experiment apparatus and procedures. Many novel and important
parameters, which were beyond reach previously, are now
successfully analyzed. These parameters include, but not limited
to, traces of path of animal's center of mass, instant and average
speed, instant and average of body turning angles, distance
traveled, turning ratio, proximity score, heading error,
stretch-and-attend, head-dipping, stay-across-arms,
supported-rearing, sniffing (exploring) at particular objects,
latency time to get to the goal (platform), time spent in specific
arm/arena or specific zones within arm/arena, number of time
entering and exiting arm/arena or specific zones within arm/arena,
and etc. These parameters provide good indications for gene
targeting, drug screening, toxicology research, learning and memory
process study, anxiety study, understanding and treatment of
diseases such as Parkinson's Disease, Alzheimer Disease, ALS, and
etc.
BRIEF DESCRIPTION OF THE DRAWINGS
[0040] FIG. 1 is a block diagram of one exemplary system
configurable to find the position, shape, and behavioral
characteristics of an object using automated video analysis,
according to one embodiment of the present invention.
[0041] FIG. 2 is a flow chart of a method of automatic video
analysis for object identification and characterization, according
to one embodiment of the present invention.
[0042] FIG. 3 is a flow chart of a method of automatic video
analysis for animal identification and characterization from video
shot from the top, according to another embodiment of the present
invention.
[0043] FIG. 4 shows an embodiment of the invention where video is
captured from the top.
[0044] FIG. 5 shows another embodiment of the invention, a top-view
based open field locomotion analysis system. Multiple arenas can be
analyzed at the same time.
[0045] FIG. 6 shows another embodiment of the invention, a top-view
based maze behavior analysis system. An elevated plus maze is
shown, but other types of mazes such as zero maze, T-maze, Y-maze,
radial arm maze can be used.
[0046] FIG. 7 shows another embodiment of the invention, a top-view
based object recognition behavior analysis system. A plurality of
objects is placed in the arena and sniffing behavior of the animal
on the objects is detected. Multiple arenas can be analyzed at the
same time.
[0047] FIG. 8 shows another embodiment of the invention, a top-view
based water maze behavior analysis system. The platform is the
target for the animal.
[0048] FIG. 9 shows another embodiment of the invention, a freezing
behavior analysis system. The animal is placed inside a chamber
where stimuli such as electric shock or auditory tones are used and
the resulting behavior after the stimuli are observed.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0049] The past few years have seen an increase in the integration
of video camera and computer technologies. Today, the integration
of the two technologies allows video images to be digitized,
stored, and viewed on small inexpensive computers, for example, a
personal computer. Further, the processing and storage capabilities
of these small inexpensive computers has expanded rapidly and
reduced the cost for performing data and computational intensive
applications. Thus, video analysis systems may now be configured to
provide robust surveillance systems that can provide automated
analysis and identification of various objects and characterization
of their behavior. The present invention provides such systems and
related methods.
[0050] In general, the present invention can automatically find the
patterns of behaviors and/or activities of a predetermined object
being monitored using video. The invention includes a system with a
video camera connected to a computer in which the computer is
configured to automatically provide object identification, object
motion tracking (for moving objects), object shape classification,
and behavior identification. In a preferred embodiment the system
includes various video analysis algorithms. The computer processes
analyze digitized video with the various algorithms so as to
automatically monitor a video image to identify, track and classify
the actions of one or more predetermined objects and its movements
captured by the video image as it occurs from one video frame or
scene to another. The system may characterize behavior by accessing
a database of object information of known behavior of the
predetermined object. The image to be analyzed may be provided in
real time from one or more camera and/or from storage.
[0051] In various exemplary embodiments described in detail as
follows, the invention is configured to enable monitoring and
classifying of animal behavior that result from testing drugs and
genetic mutations on animals. However, as indicated above, the
system may be similarly configured for use in any of a number of
surveillance or other applications. For example, the invention can
be applied to various situations in which tracking moving objects
is needed. One such situation is security surveillance in public
areas like airports, military bases, or home security systems. The
system may be useful in automatically identifying and notifying
proper law enforcement officials if a crime is being committed
and/or a particular behavior being monitored is identified. The
system may be useful for monitoring of parking security or moving
traffic at intersections so as to automatically identify and track
vehicle activity. The system may be configured to automatically
determine if a vehicle is speeding or has performed some other
traffic violation. Further, the system may be configured to
automatically identify and characterize human behavior involving
guns or human activity related to robberies or thefts. Similarly,
the invention may be capable of identifying and understanding
subtle behaviors involving portions of body such as forelimb and
can be applied to identify and understand human gesture
recognition. This could help deaf individuals communicate. The
invention may also be the basis for computer understanding of human
gesture to enhance the present human-computer interface experience,
where gestures will be used to interface with computers. The
economic potential of applications in computer-human interface
applications and in surveillance and monitoring applications is
enormous.
[0052] In one preferred embodiment illustrated in FIG. 1, the
invention includes a system in which an analog video camera 105 and
a video storage/retrieval unit 110 may be coupled to each other and
to a video digitization/compression unit 115. The video camera 105
may provide a real time video image containing an object to be
identified. The video storage/retrieval unit 110 may be, for
example, a VCR, DVD, CD or hard disk unit. The video
digitization/compression unit 115 is coupled to a computer 150 that
is configured to automatically monitor a video image to identify,
track and classify the actions (or state) of the object and its
movements (or stillness) over time within a sequence of images. The
digitization/compression unit 115 may convert analog video and
audio into, for example, MPEG format, Real Player format, etc. The
computer may be, for example, a personal computer, using either a
Windows platform or a Unix platform, or a Macintosh computer and
compatible platform. In one variation the computer may include a
number of components such as (1) a data memory 151, for example, a
hard drive or other type of volatile or non-volatile memory; (2) a
program memory 152, for example, RAM, ROM, EEPROM, etc. that may be
volatile or non-volatile memory; (3) a processor 153, for example,
a microprocessor; and (4) a second processor to manage the
computation intensive features of the system, for example, a math
coprocessor 154. The computer may also include a video processor
such as an MPEG encoder/decoder. Although the computer 150 has been
shown in FIG. 1 to include two memories (data memory 151 and
program memory 152) and two processors (processor 153 and math
co-processor 154), in one variation the computer may include only a
single processor and single memory device or more then two
processors and more than two memory devices. Further, the computer
150 may be equipped with user interface components such as a
keyboard 155, electronic mouse 156, and display unit 157.
[0053] In one variation, the system may be simplified by using all
digital components such as a digital video camera and a digital
video storage/retrieval unit 110, which may be one integral unit.
In this case, the video digitization/compression unit 115 may not
be needed.
[0054] The computer is loaded and configured with custom software
program(s) (or equipped with firmware) using, for example, MATLAB
or C/C++ programming language, so as to analyze the digitized video
for object identification and segmentation, tracking, and/or
behavior/activity characterization. This software may be stored in,
for example, a program memory 152 or data memory that may include
ROM, RAM, CD ROM and/or a hard drive, etc. In one variation of the
invention the software (or firmware) includes a unique background
subtraction method which is more simple, efficient, and accurate
than those previously known which will be discussed in detail
below.
[0055] Referring to FIG. 2, a general method of operation for one
embodiment of the invention will be described. In operation, in the
video analysis mode the system may receive incoming video images at
step 205, from the video camera 105 in real time, pre-recorded from
the video storage/retrieval unit 110, and/or a memory integral to
the computer 150. If the video is in analog format, then the
information is converted from analog to digital format and may be
compressed by the video digitization/compression unit 115. The
digital video images are then provided to the computer 150 for
various computational intensive processing to identify and segment
a predetermined object from the image. In a preferred embodiment,
the object to be identified and whose activities are to be
characterized is a moving object, for example a mouse, which has
some movement from frame to frame or scene to scene in the video
images and is generally in the foreground of the video images. In
any case, at step 210 the digital images may be processed to
identify and segregate a desired (predetermined) object from the
various frames of incoming video. This process may be achieved
using, for example, background subtraction, mixture modeling,
robust estimation, and/or other processes.
[0056] Next, at step 215, various movements (or still shapes) of
the desired object may then be tracked from one frame or scene to
another frame or scene of video images. As will be discussed in
more detail below, this tracking may be achieved by, for example,
tracking the outline contour of the object from one frame or scene
to another as it varies from shape to shape and/or location to
location. Next, at step 220, the changes in the motion of the
object, such as the shapes and locations of the object of interest
may be identified and their features extracted, and the positions
of various feature points or segments of the object such as animal
head, animal tail, animal hind body etc. may be identified and
their features extracted. Then, at step 225, the states of the
object, for example the shape, location, and feature points and
segments information may be used to characterize the objects
activity into one of a number of pre-defined behaviors or events.
For example, if the object is an animal, some pre-defined behaviors
may include walking, turning, sniffing, etc. The pre-defined
behaviors may be stored in a database in the data memory 151.
[0057] Types of behavior may also be characterized using, for
example, approaches such as rule-based label analysis or token
parsing procedure. From these methods, the system may be capable of
characterizing the object behavior as new behavior and particular
temporal rhythm.
[0058] The previous embodiments are generally applicable to
identifying, tracking, and characterizing the activities of a
particular object of interest present in a video image, e.g., an
animal, a human, a vehicle, etc. However, the invention is also
particularly applicable to the study and analysis of animals used
for testing new drugs and/or genetic mutations. As such, a number
of variations of the invention related to determining changes in
behavior of mice will be described in more detail below using
examples of video images obtained.
[0059] One variation of the present invention is designed
particularly for the purpose of automatically determining the
behavioral characteristics of a mouse. The need for sensitive
detection of novel phenotypes of genetically manipulated or
drug-administered mice demands automation of analyses. Behavioral
phenotypes are often best detected when mice are unconstrained by
experimenter manipulation. Thus, automation of analysis of behavior
in a home cage would be a preferred means of detecting phenotypes
resulting from gene manipulations or drug administrations.
Automation of analysis as provided by the present invention will
allow quantification of all behaviors and may provide analysis of
the mouse's behavior as they vary across the daily cycle of
activity. Because gene defects causing developmental disorders in
humans usually result in changes in the daily rhythm of behavior,
analysis of organized patterns of behavior across the day may be
effective in detecting phenotypes in transgenic and targeted mutant
mice. The automated system of the present invention may also detect
behaviors that do not normally occur and present the investigator
with video clips of such behavior without the investigator having
to view an entire day or long period of mouse activity to manually
identify the desired behavior.
[0060] The systematically developed definition of mouse behavior
that is detectable by the automated analysis of the present
invention makes precise and quantitative analysis of the entire
mouse behavior repertoire possible for the first time. The various
computer algorithms included in the invention for automating
behavior analysis based on the behavior definitions ensure accurate
and efficient identification of mouse behaviors. In addition, the
digital video analysis techniques of the present invention improves
analysis of behavior by leading to: (1) decreased variance due to
non-disturbed observation of the animal; (2) increased experiment
sensitivity due to the greater number of behaviors sampled over a
much longer time span than ever before possible; and (3) the
potential to be applied to all common normative behavior patterns,
capability to assess subtle behavioral states, and detection of
changes of behavior patterns in addition to individual behaviors.
Development activities have been complete to validate various
scientific definition of mouse behaviors and to create novel
digital video processing algorithms for mouse tracking and behavior
recognition, which are embody in software and hardware system
according to the present invention.
[0061] Various lighting options for videotaping have been
evaluated. Lighting at night as well as with night vision cameras
was evaluated. It has been determined that good quality video was
obtained with normal commercial video cameras using dim red light,
a frequency that is not visible to rodents. Videos were taken in a
standard laboratory environment using commercially available
cameras 105, for example a Sony analog camera, to ensure that the
computer algorithms developed would be applicable to the quality of
video available in the average laboratory. The commercially
available cameras with white lighting gave good results during the
daytime and dim red lighting gave good results at night time.
[0062] Referring again to FIG. 2, the first step in the analysis of
mouse behavior is an automated initialization step that involves
analysis of video images to identify the location and outline of
the mouse, as indicated by step 210. Second, the location and
outline of the mouse are tracked over time, as indicated by step
215. Performing the initialization step periodically may be used to
reset any propagation errors that appear during the tracking step.
As the mouse is tracked over time, its features including shape are
extracted, and used for training and classifying the posture and
body parts of the mouse from frame to frame, as indicated by step
220. Using this posture and body part information and all related
information about the orientation, shape, and position of the mouse
generated for each frame, the actual behavior is determined by
their relationship over time, as indicated by step 225.
[0063] 1. Location and Outline Identification and Feature
Extraction
[0064] The first step in analyzing a video of an animal and to
analyze the behavior of the animal is to locate and extract the
animal. A pre-generated background of the video clip in question is
first obtained and it is used to determine the foreground objects
by taking the intensity difference and applying a threshold
procedure to remove noise. This step may involve threshold
procedures on both the intensity and the size of region. An
8-connection labeling procedure may be performed to screen out
disconnected small noisy regions and improve the region that
corresponds to the mouse. In the labeling process, all pixels in a
frame will be assigned a label as foreground pixel or background
pixel based on the threshold. The foreground pixels are further
cleaned up by removing smaller components and leaving only the
largest component as the foreground object. Those foreground pixels
that border a background pixel form the contour for the object. The
outline or contour of this foreground object is thus determined.
The centroid (or center of mass) of the foreground object is
calculated and is used for representing the location of the object
(e.g., mouse).
[0065] The contour representation can be used as features of the
foreground object, in addition to other features that include but
not limited to: centroid, the principal orientation angle of the
object, the area (number of pixels), the eccentricity (roundness),
and the aspect ratio of object.
[0066] II. Mouse Tracking
[0067] Ideal tracking of foreground objects in the image domain
involves a matching operation to be performed that identifies
corresponding points from one frame to the next. This process may
become computationally too consuming or expensive to perform in an
efficient manner. Thus, one approach is to use approximations to
the ideal case that can be accomplished in a short amount of time.
For example, tracking the foreground object may be achieved by
merely tracking the outline contour from one frame to the next in
the feature space (i.e., identified foreground object image).
[0068] In one variation of the invention, tracking is performed in
the feature space, which provides a close approximation to tracking
in the image domain. The features include the centroid, principal
orientation angle of the object, area (number of pixels),
eccentricity (roundness), and the aspect ratio of object with
lengths measured along the secondary and primary axes of the
object. In this case, let S be the set of pixels in the foreground
object, A denote the area in number of pixels, (C.sub.x, C.sub.y)
denote the centroid, .phi. denote the orientation angle, E denote
the eccentricity, and R denote the aspect ratio. Then, 1 C x = 1 A
S x C y = 1 A S y
[0069] Let us define three intermediate terms, called second order
moments, 2 m 2 , 0 = S ( x - C x ) 2 m 0 , 2 = S ( y - C y ) 2 m 1
, 1 = S ( x - C x ) ( y - C y )
[0070] Using the central moments, we define, 3 = 1 2 arctan 2 m 1 ,
1 m 2 , 0 - m 0 , 2 E = ( m 2 , 0 - m 0 , 2 ) 2 + 4 m 1 , 1 2 ( m 2
, 0 + m 0 , 2 ) 2
[0071] R is equal to the ratio of the length of the range of the
points projected along an axis perpendicular to .phi., to the
length of the range of the points projected along an axis parallel
to .phi.. This may also be defined as the aspect ratio (ratio of
width to length) after rotating the foreground object by .phi..
[0072] Tracking in the feature space involves following feature
values from one frame to the next. For example, if the area
steadily increases, it could mean that the mouse is coming out of a
cuddled up position to a more elongated position, or that it could
be moving from a front view to a side view, etc. If the position of
the centroid of the mouse moves up, it means that the mouse may be
rearing up on its hind legs. Similarly, if the angle of orientation
changes from horizontal to vertical, it may be rearing up. These
changes can be analyzed with combinations of features also.
[0073] However, it is possible for a contour representation to be
used to perform near-optimal tracking efficiently in the image
domain (i.e., the complete image before background is
subtracted).
[0074] III. Mouse Feature Points and Segments Identification
[0075] Once the features are obtained for the frames in the video
sequence, the foreground state of the mouse is determined by
identifying certain important feature points and segments on the
mouse such as the head, tail, waist, fore body and hind body.
[0076] The head is detected by using a combination of features
including direction of motion, distance to tip from the center of
mass, the curvature at that point, and the tail information. The
head is not on the side of the tail, but, on the other side.
[0077] The tail is detected using a combination of shape features
including thickness information and distance from center of
mass.
[0078] The waist is detected by determining the minor axis of the
ovoid shape of the animal after having the tail removed from
consideration.
[0079] The portion of the body in front of the waist towards the
head is called the fore body and the portion of the body aft of the
waist towards the tail is called the hind body.
[0080] Using these identified points and segments, various
parameters such as the orientation, heading direction, turning
angle, proximity to other objects or zone boundaries, etc. are
obtained.
[0081] IV. Behavior Detection Methodology
[0082] Each behavior can be modeled as a set of rules or conditions
that must be satisfied. The rules or conditions can be formulated
using any of the available features or parameters including
position and shape of specific body parts with or without respect
to other objects, motion characteristics of the entire mouse body
or individual body parts, etc. In the descriptions below, all such
rules or conditions that are required to derive the specific
modeling of the behavior are stated. The behavior descriptions
follow:
[0083] A. Freeze
[0084] Freezing behavior is determined by the absence of movement
of rodent body for a brief period of time. Freezing behavior is
primarily used in conditioning-fear experiments where auditory
tones or electric shock is administered to the animal to cause fear
leading to freezing.
[0085] B. Sniff at Objects
[0086] Sniffing behavior is determined by the touching of the mouth
of the rodent body against another defined object, or animal. A
zone or area is calibrated to represent this target object or
animal, and any encroachment or contact of the head/mouth of the
rodent body against or into this target zone is detected as a
sniff.
[0087] C. Locomote
[0088] Locomotion behavior is defined as the movement behavior of
the rodent around the cage or arena. Locomotion behavior is best
viewed from the top thereby allowing accurate measurements of the
total distance traveled, speed, acceleration, heading direction,
turning angle, and distance to a specific target.
[0089] D. Stretch and Attend
[0090] Stretch and Attend behavior is determined by the purposeful
extension of the head portion of the rodent body forward and a
subsequent retraction of the head while the hind part of the rodent
body remains stationary, when viewed from the top. The extension of
the head may involve bending it to the side.
[0091] E. Head Dip
[0092] Head Dipping behavior is determined by the downward movement
of the head of the animal over a ledge or a platform, as if to look
below. This can be either intentional or unintentional.
[0093] F. Transgress From One Area to Another
[0094] Transgression behavior is detected by the movement of a
portion of, or the entire body of the rodent across from one
defined zone or area into another defined zone or area. With this
behavior, exit and entrance measures with respect to a zone can be
calculated.
[0095] V. Behavior Identification
[0096] Using the feature data assigned for each of the frames in
the video clip, the approach is to determine those behaviors and
events as defined in the previous step. This process will be
accomplished in real-time so that immediate results will be
reported to investigators or stored in a database. One approach is
to use a rule-based analysis procedure by which various features
are analyzed and if these features fit certain criteria then, that
particular behavior or event is detected. For example, "when the
mouth touches a object in an object recognition paradigm and
remains in contact for a minimum duration of time and then,
releases contact with that object and remains out of contact for a
certain minimum duration of time, that episode is called
"Sniffing".
[0097] In summary, when a new video clip is analyzed, the system of
the present invention first obtains the video image background and
uses it to identify the foreground objects. Then, features are
extracted from the foreground objects and various important feature
points and segments on the animal are identified. This set of
features is passed to a behavior identification system module that
identifies the final set of behaviors or events for the video clip.
The image resolution of the system that has been obtained and the
accuracy of identification of the behaviors attempted so far have
been very good and resulted in an effective automated video image
object recognition and behavior characterization system.
[0098] The invention may identify some abnormal behavior by using
video image information (for example, stored in memory) of known
abnormal animals to build a video profile for that behavior.
Further, abnormalities may also result from an increase in any
particular type of normal behavior. Detection of such new abnormal
behaviors may be achieved by the present invention detecting, for
example, segments of behavior that do not fit the standard profile.
The standard profile may be developed for a particular strain of
mouse whereas detection of abnormal amounts of a normal behavior
can be detected by comparison to the statistical properties of the
standard profile. Thus, the automated analysis of the present
invention may be used to build a profile of the behaviors, their
amount, duration, and daily cycle for each animal, for example each
commonly used strain of mice. A plurality of such profiles may be
stored in, for example, a database in a data memory of the
computer. One or more of these profiles may then be compared to a
mouse in question and difference from the profile expressed
quantitatively.
[0099] The techniques developed with the present invention for
automation of the categorization and quantification of all mouse
behaviors is a powerful tool for detecting phenotypic effects of
gene manipulations in mice. As previously discussed, this
technology is extendable to other behavior studies of animals and
humans, as well as surveillance purposes. In any case, the present
invention has proven to be a significant achievement in creating an
automated system and methods for automated accurate identification,
tracking and behavior categorization of an object whose image is
captured in a video image.
[0100] In another preferred embodiment of the invention, there are
multiple cameras taking video images of experiment apparatus that
contain animals. There is at least one apparatus, but, as many as
the computer computing power allows, say four (4) or sixteen (16)
or even more, can be analyzed. See FIG. 5. Each apparatus 510
contains at least one animal or multiple animals. The single or
multiple cameras 505 may be taking video from different points of
views such as one taking video images from the side of the
apparatus, or one taking video images from the top of the
apparatus. These apparatus can be home cage, open field cage, water
maze device, T-maze device, Y-maze device, radial arm maze device,
zero maze device, elevated plus maze device, or other experiment
devices. The invention can also be applied to various experimental
paradigms such as object recognition, and conditioned fear freezing
experiments. When video images are taken of multiple apparatuses
and devices containing one or multiple animals, and are analyzed
for identifying these animals' behaviors, high throughput screening
is achieved. When video images taken from different points of
views, for example, one from the top view and another from the side
view, are combined to identify animal's behaviors, integrated
analysis is achieved.
[0101] A variation of the present invention is directed to
automatically determining locomotion behavior of mice or rats in an
open field 311. Once the animal is identified as a foreground
object 308 as discussed above, the body parts of the animal such as
head and tail, and hind limbs and forelimbs, and center of mass,
are identified 310. The traces of the path of the movements of the
animal's center of mass in the open field under observation is
recorded, its instant and average speed of movements and distance
traveled are calculated, its instant and cumulative body turning
angles are analyzed 311. In addition, events like turning ratio
(ratio of path length over number of turns, where number of turns
is counted when the animal makes a turn larger than 80 degrees when
the animal travels one body length); proximity score (calculated by
determining the distance of the animal from the goal during each
second of the trial and is used as a measure of deviation from the
ideal path to the platform once an animal is placed in the cage);
heading errors (defined as an instance of moving away from a
target); and animal staying in a specific zone inside the field,
are recorded 316. Then a visualization process will further analyze
the result of the path trace recorded to generate variety of
statistic results. Visualization process allows users to use
graphic drawing tools to define any number of zones of any shape in
the open field as needed. The system provides a graphic tool that
allows users to define the field of any shape. An example apparatus
is shown in FIG. 5, where a camera 505, placed directly above the
arenas 510, captures video from the top. A plurality of the arenas
may be used and their video captured using a single camera or
multiple cameras to achieve high-throughput analysis. The
locomotion or movement behavior of the animal in each arena is
analyzed.
[0102] Another variation of the present invention is directed to
automatically determining the object recognition activity 312.
Graphic tools are provided to allow users to define objects in the
scene. Once the animal is identified as a foreground object 308 as
discussed above, the body parts of the animal such as head and tail
and hind limbs and forelimbs, and center of mass are identified
310. The traces of the path of the movements of the animal's center
of mass are recorded. The distances of the animal's head to any of
the objects in the scene are calculated and when the distance to an
object is less than a user-defined amount, the animal is counted as
animal's sniffing at the object and is said to be exploring that
object 312. Statistics about these exploring events are generated
and exported 317. An example apparatus is shown in FIG. 7, where a
camera 705, placed directly above the arena 710, capture video from
the top. A plurality of objects, in this case two objects 715 720
are placed in the arena and the exploratory or sniffing behaviors
of the animal on these objects are analyzed.
[0103] Another variation of the previous embodiments is
particularly applicable to the study and analysis of mice or rats
in their spatial learning and memory. This third variation of the
present invention is directed towards automatically determining the
behaviors of mice or rats in a water maze experiment environment
315. Graphic tools are provided to allow users to define the maze
and platforms. Once the animal is identified as a foreground object
308 as discussed above, the body parts of the animal such as head
and tail and hind limbs and forelimbs, and center of mass are
identified 310. The traces of the path of the movements of the
animal's center of mass are recorded. The latency (the time period
the animal spent in swimming in the water before landing at the
platform) is measured; its instant and average speed of movements
and distance traveled are calculated; its instant and cumulative
body turning angles are analyzed 315. In addition, events like
turning ratio (ratio of path length over number of turns, where
number of turns is counted when the animal makes a turn larger than
90 degrees when the animal travels one body length); proximity
score (calculated by determining the distance of the animal from
the goal (platform) during each second of the trial and is used as
a measure of deviation from the ideal path to the platform once an
animal is placed in the water); heading errors (defined as an
instance of swimming away from the VISIBLE platform); and animal
staying in a specific zone inside the maze, are recorded 320. Then
a visualization process will further analyze the result of the path
trace recorded to generate variety of statistic results.
Visualization process allows users to use graphic drawing tools to
define any number of zones of any shape in the open field as
needed. The system provides a graphic tool that allows users to
define the field of any shape. An example apparatus is shown in
FIG. 8, where a camera 805, placed directly above the water tank
810, captures video from the top and the movement behavior of the
animal inside the water tank and its relationship with the target
platform 815 is analyzed.
[0104] Another variation of the previous embodiments is
particularly applicable to the study and analysis of mice or rats
in their spatial learning and memory and anxiety. This fourth
variation of the present invention is directed towards
automatically determining the behaviors of mice or rats in a
variety of maze apparatus 313. Graphic tools are provided to define
specific maze apparatus, such as T-maze, Y-maze, radial arm maze,
zero maze, elevated plus maze, and etc. Once the animal is
identified as a foreground object 308 as discussed above, the body
parts of the animal such as head and tail and hind limbs and
forelimbs, and center of mass are identified 310. The traces of the
path of the movements of the animal's center of mass are recorded.
More importantly, the animal's behaviors related to every arm in
the maze, such as time spent in each arm, the number of times
entering and exiting an arm, are found 313. Besides, animal's
instant and average speed of movements and distance traveled are
calculated; its instant and cumulative body turning angles are
analyzed 318. In addition, events such as animal partial incursions
into particular arm (for example, the animal might maintain its
hind quarters in a closed arm while poking its nose into an open
arm); Stretch-Attend Behavior; Head-Dipping behavior; and Supported
Rearing, are detected 313. An example apparatus is shown in FIG. 6
where an elevated plus maze 610 is used to analyze the behavior of
the animal. A camera 605, placed directly above, capture video from
the top and analyzes the movement behaviors in various arms or
areas of the maze.
[0105] Fifth variation of the present invention is directed to
automatically determining the freezing behaviors of mice or rats in
a cued or conditioned fear tests 314. Graphic tools are provided.
Graphic tools are provided to define the area within which animal
activity is measured. Differences between neighboring frames are
compared pixel-by-pixel in terms of their intensity and color
intensity. These differences are used to calculate the motion of
the animal from frame-to-frame because motion in the area is caused
by movements of the animal. The values of these differences
indicate if the animal is moving or freezing 314. An example
apparatus is shown is FIG. 9. The camera 905 maybe placed inside or
outside the chamber 910. The animal is placed inside the chamber
and stimuli in the form of auditory tones 915 or electric shock 920
is presented and their behavioral effects following the stimuli are
analyzed.
[0106] The systematically developed definitions of mouse behaviors
that are detectable by the automated analysis according to the
present invention makes precise and quantitative analysis of the
entire mouse behavior repertoire possible for the first time. The
various computer algorithms included in the invention for
automating behavior analysis based on the behavior definitions
ensure accurate and efficient identification of mouse behaviors. In
addition, the digital video analysis techniques of the present
invention improves analysis of behavior by leading to: (1)
decreased variance due to non-disturbed observation of the animal;
(2) increased experiment sensitivity due to the greater number of
behaviors sampled over a much longer time span than ever before
possible; and (3) the potential to be applied to all common
normative behavior patterns, capability to assess subtle behavioral
states, and detection of changes of behavior patterns in addition
to individual behaviors.
[0107] Although particular embodiments of the present invention
have been shown and described, it will be understood that it is not
intended to limit the invention to the preferred or disclosed
embodiments, and it will be obvious to those skilled in the art
that various changes and modifications may be made without
departing from the spirit and scope of the present invention. Thus,
the invention is intended to cover alternatives, modifications, and
equivalents, which may be included within the spirit and scope of
the invention as defined by the claims.
[0108] For example, the present invention may also include audio
analysis and/or multiple camera analysis. The video image analysis
may be augmented with audio analysis since audio is typically
included with most video systems today. As such, audio may be an
additional variable used to determine and classify a particular
objects behavior. Further, in another variation, the analysis may
be expanded to video image analysis of multiple objects, for
example mice, and their social interaction with one another. In a
still further variation, the system may include multiple cameras
providing one or more planes of view of an object to be analyzed.
In an even further variation, the camera may be located in remote
locations and the video images sent via the Internet for analysis
by a server at another site. In fact, the standard object behavior
data and/or database may be housed in a remote location and the
data files may be downloaded to a stand alone analysis system via
the Internet, in accordance with the present invention. These
additional features/functions add versatility to the present
invention and may improve the behavior characterization
capabilities of the present invention to thereby achieve object
behavior categorization which is nearly perfect to that of a human
observer for a broad spectrum of applications.
[0109] All publications, patents, and patent applications cited
herein are hereby incorporated by reference in their entirety for
all purposes.
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