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Most existing multiple object tracking techniques suffer from the well-known "multi-object occlusion" problem and/or immense computational cost due to the use of high- dimensional joint state representation. In this paper, we present a novel distributed Bayesian framework using multiple collaborative cameras for robust and efficient multiple object tracking with significant and persistent occlusion. When the objects are in close proximity or present multi-object occlusions in a particular camera view, camera collaboration between different views is activated in order to handle the multi-object occlusion problem. Moreover, we propose to model the camera collaboration likelihood density by using epipolar geometry with particle filter implementation. The performance of our approach has been demonstrated on real-world video data.