A Bayesian Non-Parametric Viewpoint to Visual Tracking

Yi Wang, Zhidong Li, Yang Wang, Fang Chen

IEEE Workshop on Applications of Computer Vision

A novel bayesian non-parametric method for tracking is proposed in this paper. The foreground appearance distribution is modeled by unbounded mixtures controlled through a Bayesian non-parametric process. Two posterior inference strategies are provided: Gibbs sampling and sequential importance sampling. Both of these two sampling strategy benefits from the conjugate prior/posterior pairs by factorizing the joint posterior distributions. Once the mixture model is obtained/updated, the similarities/probablity of each observations assigned to this mixture model could be easily calculated. In model matching/verification, the Kullback-Leibler divergence and texture information is adopted for verification purpose. The robustness of our methods is demonstrated by the experiments.

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