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Visual Tracking Using High-Order Particle Filtering
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DOI：10.1109/LSP.2010.2091406.png)
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Abstract
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In this letter, we extend the first-order Markov chain model commonly used in visual tracking and present a novel framework of visual tracking using high-order Monte Carlo Markov chain. By using graphical models to obtain conditional independence properties, we derive a general expression for the posterior density function of an mth-order hidden Markov model. We subsequently use Sequential Importance Sampling (SIS) to estimate the posterior density and obtain the high-order particle filtering algorithm for visual object tracking. Experimental results demonstrate that the performance of our proposed algorithm is superior to traditional first-order particle filtering (i.e., particle filtering derived based on first-order Markov chain).
Keywords:
High-order Markov chain
graphical models
particle filtering
visual tracking
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