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

Jungong Han, Minwei Feng, P.H.N. With, de

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

9 Citations (Scopus)
69 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publicationIEEE International Conference on Multimedia and Expo, 2008 : Hannover, Germany, 23 - 26 June 2008
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages305-308
ISBN (Print)978-1-424-42571-6
DOIs
Publication statusPublished - 2008

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