A novel algorithm is proposed to perform object tracking with multiple cameras in the Bayesian Inference framework. Firstly, Bayesian network is used to model the system of multiple static cameras’ tracking system. Then, the high-dimensional joint posterior of the state (object location) is propagated spatiotemporally. Finally, the estimation of the target location in each camera view is achieved by using an efficient message passing mechanism with sequential Monte Carlo Approximation (particle filter) of the joint posterior. Meanwhile, by taking full advantage of image data and position data from multiple cameras, the tracking algorithm is very robust to occlusion in some cameras of the system. Both qualitative and quantitative experiments have demonstrated the effectiveness and robustness of the proposed algorithm.