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Markov chain Monte Carlo-based multi-object visual tracking has been investigated here. To improve the confidence of sampling and perform the iteration effectively, a new approach to multi-object visual tracking is proposed based on reversible jump Markov chain Monte Carlo sampling. The tracking problem is formulated as computing the maximum a posteriori estimation given image observations. Four types of reversible and jump moves are designed for Markov chains dynamics, and prior proposal distributions of objects are developed with the aid of association match matrix. The joint likelihood distribution measurement is presented at two levels of clustered blocks subsets and pixels. Experimental results and quantitative evaluation demonstrate that the proposed approach is effective for challenge situations.