A dynamic bayesian network-based framework for visual tracking

Authors:
Hang-Bong Kang;Sang-Hyun Cho
Affiliations:
Dept. of Computer Engineering, Catholic University of Korea, Puchon City Kyonggi-Do, Korea;Dept. of Computer Engineering, Catholic University of Korea, Puchon City Kyonggi-Do, Korea
Venue:
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Year:
2005

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Abstract

In this paper, we propose a new tracking method based on dynamic Bayesian network. Dynamic Bayesian network provides a unified probabilistic framework in integrating multi-modalities by using a graphical representation of the dynamic systems. For visual tracking, we adopt a dynamic Bayesian network to fuse multi-modal features and to handle various appearance target models. We extend this framework to multiple camera environments to deal with severe occlusions of the object of interest. The proposed method was evaluated under several real situations and promising results were obtained.