Visual tracking in high-dimensional state space by appearance-guided particle filtering |
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Authors: | Wen-Yan Chang Chu-Song Chen Yong-Dian Jian |
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Affiliation: | Institute of Information Science, Academia Sinica, Taipei, Taiwan, ROC. wychang@iis.sinica.edu.tw |
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Abstract: | In this paper, we propose a new approach, appearance-guided particle filtering (AGPF), for high degree-of-freedom visual tracking from an image sequence. This method adopts some known attractors in the state space and integrates both appearance and motion-transition information for visual tracking. A probability propagation model based on these two types of information is derived from a Bayesian formulation, and a particle filtering framework is developed to realize it. Experimental results demonstrate that the proposed method is effective for high degree-of-freedom visual tracking problems, such as articulated hand tracking and lip-contour tracking. |
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