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.

• Type: Conference
• Year: 2008
• Where: ICMCS
• Authors: Jungong Han, Minwei Feng, Peter H. N. de With
• Keywords: Gaussian Mixture Model, Human Silhouettes, ICMCS 2008, Markov Random Fields, Multimedia