A Bayesian approach to joint tracking and identification of geometric shapes in video sequences
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摘要
A Bayesian approach is proposed for joint tracking and identification. These two problems are often addressed independently in the literature, leading to suboptimal performance. In a Bayesian approach, a prior distribution is set on both the hypothesis space and the associated parameter space. Although this is straightforward from a conceptual viewpoint, it is typically impossible to perform inference in closed-form. We discuss an advanced particle filtering approach to solve this computational problem and apply this algorithm to joint tracking and identification of geometric forms in video sequences.
论文关键词:Bayesian model selection,Particle filtering,Target tracking
论文评审过程:Received 21 November 2007, Revised 24 April 2009, Accepted 2 May 2009, Available online 13 May 2009.
论文官网地址:https://doi.org/10.1016/j.imavis.2009.05.002