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ICMCS
2008
IEEE

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

8 years 8 months ago
A real-time video surveillance system with human occlusion handling using nonlinear regression
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- lter with the bene t of being a factor of 10-20 faster in computing.
Jungong Han, Minwei Feng, Peter H. N. de With
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICMCS
Authors Jungong Han, Minwei Feng, Peter H. N. de With
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