本文针对多摄像机视频跟踪的应用场景，在贝叶斯推理的框架下，提出了一种具有分布式时空交互计算特点的目标跟踪算法。本文首先利用贝叶斯网络对拓扑确定的多摄像机系统进行建模，并对待估状态(目标位置)高阶联合后验概率密度函数进行时空的递推，最后借助序列蒙特卡洛(粒子滤波)逼近后验概率密度函数，并采用高效的数据传送机制高效求解出跟踪目标在各个摄像机视野中状态估计值。通过序贯蒙特卡洛粒子滤波融合时空的目标灰度及位置信息，有效地抑制了部分摄像机内遮挡现象给跟踪造成的影响。定性和定量实验均证明了该算法的鲁棒性和高效性。

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.