Abstract: MCMC-based multi-object visual tracking was investigated here. To improve the confidence of sampling and perform the iteration effectively,a new approach to multi-object visual tracking was proposed based on reversible jump Markov chain Monte Carlo (RJMCMC) sampling. Uiven image observation, the tracking problem was formulated as computing the MAP (maximum a posteriori) estimation .The prior proposal distribution of object was developed with the aid of association match matrix,and four types of reversible and jump moves were designed for Markov chains dy- namics. I}he likelihood distribution measure was presented via position-weighted colour Kist match between reference objects and candidate objects. The state updating was generated from mean-shift(MS) iteration,rather than from random walk in the MCMC sampling. Experimental results and quantitative evaluation demonstrate that the proposed approach is effective for challenge situations.

Key words: Visual mufti-objects trakcing,Reversible jump Markov chain Monte Carlo(RJMCMC) sampling,I3ayes inferencc, Mcan-shift