In this paper, we present a novel decentralized Bayesian frame-work using multiple collaborative cameras for robust and efficient multiple object tracking with significant and persistent occlusion. This approach avoids the common practice of using a complex joint state representation and a centralized processor for multiple camera tracking. 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. Specifically, 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 both synthetic and real-world video data.