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Optical Engineering

Robust event detection scheme for complex scenes in video surveillance
Author(s): Erkang Chen; Yi Xu; Xiaokang Yang; Wenjun Zhang
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Paper Abstract

Event detection for video surveillance is a difficult task due to many challenges: cluttered background, illumination variations, scale variations, occlusions among people, etc. We propose an effective and efficient event detection scheme in such complex situations. Moving shadows due to illumination are tackled with a segmentation method with shadow detection, and scale variations are taken care of using the CamShift guided particle filter tracking algorithm. For event modeling, hidden Markov models are employed. The proposed scheme also reduces the overall computational cost by combing two human detection algorithms and using tracking information to aid human detection. Experimental results on TRECVid event detection evaluation demonstrate the efficacy of the proposed scheme. It is robust, especially to moving shadows and scale variations. Employing the scheme, we achieved the best run results for two events in the TRECVid benchmarking evaluation.

Paper Details

Date Published: 1 July 2011
PDF: 9 pages
Opt. Eng. 50(7) 077204 doi: 10.1117/1.3596603
Published in: Optical Engineering Volume 50, Issue 7
Show Author Affiliations
Erkang Chen, Univ. of Michigan-Shanghai Jiao Tong Univ. Joint Institute (China)
Yi Xu, Shanghai Jiao Tong Univ. (China)
Xiaokang Yang, Shanghai Jiao Tong Univ. (China)
Wenjun Zhang, Shanghai Jiao Tong Univ. (China)


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