Image analysis – Applications – Target tracking or detecting
Reexamination Certificate
2006-10-10
2008-11-25
Kim, Charles (Department: 2624)
Image analysis
Applications
Target tracking or detecting
C382S160000, C348S169000
Reexamination Certificate
active
07457436
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.
REFERENCES:
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Rigoll, et al., “Robust Person Tracking in Real Scenarios with Non-Stationary Background Using a Statistical Computer Vision Approach” Visual Surveillance, 1999. Second IEEE Workshop on, (VS'99), Jun. 26, 1999 pp. 41-47. Digital Object Identifier 10.1109/VS.1999.780267.
Coetzee Frans
Paragios Nikos
Ramesh Visvanathan
Stenger Bjoern
Kim Charles
Paschburg Donald B.
Siemens Corporate Research Inc.
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