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

  • Authors:

  • Ching-Chun Huang;Sheng-Jyh Wang

  • Affiliations:

  • Department of Electronics Engineering, National Chiao Tung University, Hsinchu, Taiwan;Department of Electronics Engineering, National Chiao Tung University, Hsinchu, Taiwan

  • Venue:
  • ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
  • Year:
  • 2009

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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.