Method and apparatus for automated video activity analysis
First Claim
1. A method for detection the parts of the non-rigid object such as, but not limited to, human body, and for recognition gestures and activities of the object of interest in real-time video sequences comprising:
- a) Eliminating the background to obtain the foreground objects;
b) Detecting different regions of the foreground objects by using color and/or shape information;
c) Finding the contours of the areas cited in 1b);
d) Fitting closed curves to the contours cited in 1c);
e) Computing unary and binary attributes for the closed curves cited in 1d);
f) Comparing the attributes cited in 1e) with the object attributes in the training data set via a matching algorithm and determining object parts after matching;
g) Combining adjacent segments and repeating claims 1c) through 1f);
h) Storing 2D center of gravity coordinates of each object part in the buffers for certain number of frames;
i) Comparing the change of center of gravity coordinates with time for each object part cited in 1h) with the templates in the training data set and recognizing the activity of each part separately;
j) Combining the activities of the object parts cited in 1h) and recognizing the overall activity of the object of interest in the scene; and
, k) Combining the gestures and activities of different objects to detect the event in the scene.
1 Assignment
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Accused Products
Abstract
The invention is a new method and apparatus that can be used to detect, recognize, and analyze people or other objects in security checkpoints, public-places, parking lots, or in similar environments under surveillance to detect the presence of certain objects of interests (e.g., people), and to identify their activities for security and other purposes in real-time. The system can detect a wide range of activities for different applications. The method detects any new object introduced into a known environment and then classifies the object regions to human body parts or to other non-rigid and rigid objects. By comparing the detected objects with the graphs from a database in the system, the methodology is able to identify object parts and to decide on the presence of the object of interest (human, bag, dog, etc.) in video sequences. The system tracks the movement of different object parts in order to combine them at a later stage to high-level semantics. For example, the motion pattern of each human body part is compared to the motion pattern of the known activities. The recognized movements of the body parts are combined by a classifier to recognize the overall activity of the human body.
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Citations
22 Claims
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1. A method for detection the parts of the non-rigid object such as, but not limited to, human body, and for recognition gestures and activities of the object of interest in real-time video sequences comprising:
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a) Eliminating the background to obtain the foreground objects;
b) Detecting different regions of the foreground objects by using color and/or shape information;
c) Finding the contours of the areas cited in 1b);
d) Fitting closed curves to the contours cited in 1c);
e) Computing unary and binary attributes for the closed curves cited in 1d);
f) Comparing the attributes cited in 1e) with the object attributes in the training data set via a matching algorithm and determining object parts after matching;
g) Combining adjacent segments and repeating claims 1c) through 1f);
h) Storing 2D center of gravity coordinates of each object part in the buffers for certain number of frames;
i) Comparing the change of center of gravity coordinates with time for each object part cited in 1h) with the templates in the training data set and recognizing the activity of each part separately;
j) Combining the activities of the object parts cited in 1h) and recognizing the overall activity of the object of interest in the scene; and
,k) Combining the gestures and activities of different objects to detect the event in the scene. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 20)
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15. A method for detection human body postures and for recognition global direction of the body in compressed domain, said method comprising:
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a) Eliminating background to obtain the foreground object in compressed domain;
b) Windowing the foreground object cited in 15a) and scaling the window in compressed domain by using the human body proportions;
c) Comparing AC coefficients of the scaled window cited in 15b) with the AC coefficients of different human postures in the database and recognizing posture of the human in the scene; and
,d) Comparing horizontal AC coefficient differences with the vertical AC coefficient differences and obtaining global activity of the body in compressed domain. - View Dependent Claims (16, 17, 18, 19, 21)
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22. A method for detection the rigid object parts and for recognition activities of a rigid object in real-time video sequences comprising:
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a) Eliminating the background to obtain the foreground objects;
b) Detecting different regions of the foreground objects by using color and/or shape information;
c) Finding the contours of the areas cited in 22b);
d) Fitting closed curves to the contours cited in 22c);
e) Computing unary and binary attributes for the closed curves cited in 22d);
f) Repeating claims 22a) through 22e) and comparing the object attributes with the model object attributes in the training data set via a matching algorithm and determining the object parts;
g) Combining adjacent segments and repeating claim 22f);
h) Storing 2D center of gravity coordinates of rigid object parts in the buffer for certain number of frames;
i) Comparing the change of center of gravity coordinates with time for each object part cited in 22h) with the templates in the training data set and recognizing the activity of object parts; and
,j) Combining the activities of the object parts cited in 22i) and recognizing the overall activity of the object in the scene.
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Specification