Real-time crowd density estimation from video

Details Real-time crowd density estimation from video Real-time crowd density estimation from video

2006-11-21

2006-11-21

Mehta, Bhavesh M. (Department: 2624)

Image analysis

Applications

Target tracking or detecting

C382S106000, C356S004030

Reexamination Certificate

active

07139409

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.

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Papadopoulos et al, “Parallel Processing of digital images using Image Processing”, IEE 6th Int. conf. On DSP in Communications, Sep. 1991: This reference teaches the geometric correction performed by Velastin.
Cho et al, “A neural-based crowd estimation by hybrid global learning algorithm” Systems, Man and Cybernetics, Part B, IEEE Transactions on, vol. 29, Issue 4, Aug. 1999 pp. 535-541: Another method for determining congestion on a platform.
Kamijo et al, ‘Traffic Monitoring and Accident Detection at Intersections’ IEEE Transactions on Intelligent Transportation Systems, vol. 1, No. 2, Jun. 2000.
Stenger et al, ‘Topology Free Hidden Markov Models : Application to Background Modeling’ Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on vol. 1, Jul. 7-14, 2001 pp. 294-301 vol. 1.

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