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A real-time video surveillance system with human occlusion handling using nonlinear regression

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3 Author(s)
Han, J. ; Univ. of Technol. Eindhoven, Eindhoven ; Minwei Feng ; de With, P.H.N.

This paper presents a real-time single-camera surveillance system, aiming at detecting and partly analyzing a group of people. A set of moving persons is segmented using a combination of the Gaussian Mixture Model (GMM) and the Dynamic Markov Random Fields (DMRF) technique. For a better extraction of the human silhouettes, the energy function of DMRF is extended with texture information. The mean-shift algorithm is utilized to track multiple people over the sequence. To address the human-occlusion problem, we model the horizontal projection histograms of the human silhouettes using a nonlinear regression algorithm. This model enables to automatically locate the people during the occlusions. Experiments show that the proposal has nearly same performance (also with occlusion) as the particle-filter with the benefit of being a factor of 10-20 faster in computing.

Published in:

Multimedia and Expo, 2008 IEEE International Conference on

Date of Conference:

June 23 2008-April 26 2008