Real-time crowd density estimation from video
First Claim
1. A method of video analysis comprising the steps of:
- estimating a background reference model comprising of a plurality of states for representing a background;
estimating geometric parameters for representing size changes of objects as the objects are moved at various depths in a given frame, the geometric parameters comprising a weighting for each pixel in the given frame;
providing input video data comprising a plurality of input frames;
obtaining a change detection map for distinguishing the background from the objects in each input frame, based on a Markov Random Field approach wherein a difference between an input frame and a most probable background reference state is analyzed using a statistical model, wherein information regarding expected background properties and normalized color background properties is combined with spatial and neighborhood constraints and temporal differences due to object motion in the context of said MRF using local dependencies to ensure smoothness; and
determining a measure of congestion of the given frame by combining the change detection map with the geometric parameters.
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Abstract
A system and method for automated and/or semi-automated analysis of video for discerning patterns of interest in video streams. In a preferred embodiment, the present invention is directed to identifying patterns of interest in indoor settings. In one aspect, the present invention deals with the change detection problem using a Markov Random Field approach where information from different sources are naturally combined with additional constraints to provide the final detection map. A slight modification is made of the regularity term within the MRF model that accounts for real-discontinuities in the observed data. The defined objective function is implemented in a multi-scale framework that decreases the computational cost and the risk of convergence to local minima. To achieve real-time performance, fast deterministic relaxation algorithms are used to perform the minimization. The crowdedness measure used is a geometric measure of occupancy that is quasi-invariant to objects translating on the platform.
42 Citations
16 Claims
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1. A method of video analysis comprising the steps of:
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estimating a background reference model comprising of a plurality of states for representing a background; estimating geometric parameters for representing size changes of objects as the objects are moved at various depths in a given frame, the geometric parameters comprising a weighting for each pixel in the given frame; providing input video data comprising a plurality of input frames; obtaining a change detection map for distinguishing the background from the objects in each input frame, based on a Markov Random Field approach wherein a difference between an input frame and a most probable background reference state is analyzed using a statistical model, wherein information regarding expected background properties and normalized color background properties is combined with spatial and neighborhood constraints and temporal differences due to object motion in the context of said MRF using local dependencies to ensure smoothness; and determining a measure of congestion of the given frame by combining the change detection map with the geometric parameters. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform method steps for video analysis comprising the steps of:
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estimating a background reference model comprising of a plurality of states for representing a background; estimating geometric weights for each pixel for representing a scale variation of objects in a given frame; providing input video data comprising a plurality of input frames; obtaining a change detection map for distinguishing the background from the objects in each input frame, based on a Markov Random Field approach wherein a difference between an input frame and a most probable background reference state is analyzed using a statistical model, wherein information regarding expected background properties and normalized color background properties is combined with spatial and neighborhood constraints and temporal differences due to object motion in the context of said MRF using local dependencies to ensure smoothness; and determining a measure of congestion of the given frame by combining the change detection map with the geometric weights. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A method of video analysis comprising the steps of:
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estimating a background reference model comprising of a plurality of states representing a platform area; estimating geometric parameters for representing size changes of objects as the objects move at various depths on the platform area in a given frame, the geometric parameters comprising a weighting for each pixel of the people in the given frame; obtaining a change detection map for distinguishing the background from the objects in the given frame, based on a Markov Random Field approach wherein a difference between the given frame and a most probable background reference state is analyzed using a statistical model, wherein information regarding expected background properties and normalized color background properties is combined with spatial and neighborhood constraints and temporal differences due to object motion in the context of said MRF using local dependencies to ensure smoothness; and determining a measure of congestion of the platform area by combining the change detection map with the geometric parameters, wherein the measure of congestion comprises a prolonged temporal event wherein a given percentage of the platform area is crowded with the people for a given period of time. - View Dependent Claims (16)
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Specification