Visual tracking using high-order Monte Carlo Markov chain

In this paper, we discard the first-order Markov state-space model commonly used in visual tracking and present a framework of visual tracking using high-order Monte Carlo Markov chain. By using graphic models to obtain the conditional independence properties, we derive the general expression of posterior density function for the mth-order hidden Markov model. We subsequently use Sequential Importance Sampling method to estimate the posterior density and obtain the high-order particle filtering algorithm for tracking. Experimental results show the superior performance of our proposed algorithm to traditional first-order particle filtering tracking algorithm, i.e. particle filtering derived based on first-order Markov chain.