Moving targets labeling and correspondence over multi-camera surveillance system based on Markov network

Ching-Chun Huang*, Sheng-Jyh Wang

*Corresponding author for this work

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

2 Scopus citations

Abstract

In this paper, we propose an efficient way to simultaneously label and map targets over a multi-camera surveillance system. In the system, we first fuse the detection results from multiple cameras into a posterior distribution. This distribution indicates the likelihood of having some moving targets on the ground plane. Based on the distribution, isolated targets, together with their 3-D positions, are identified in a sample-based manner, which combines Markov Chain Monte Carlo (MCMC), and Mean-Shift clustering. The induced 3-D scene information is further inputted into a 3-layer Bayesian hierarchical framework (BHF), which adopts a Markov network to deal with the object labeling and correspondence problems. In principle, labeling and correspondence are regarded as a unified optimal problem subject to 3-D scene prior, image color similarity, and detection results. The experiments show that accurate results can be gotten even under situations with severe occlusion.

Original languageEnglish
Title of host publicationProceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009
Pages1258-1261
Number of pages4
DOIs
StatePublished - 20 Nov 2009
Event2009 IEEE International Conference on Multimedia and Expo, ICME 2009 - New York, NY, United States
Duration: 28 Jun 20093 Jul 2009

Publication series

NameProceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009

Conference

Conference2009 IEEE International Conference on Multimedia and Expo, ICME 2009
CountryUnited States
CityNew York, NY
Period28/06/093/07/09

Keywords

  • Graphical models
  • Image labeling
  • Markov Chain Monte Carlo
  • Mean-shift
  • Object correspondence

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