This paper proposes a distributed algorithm for object tracking in a camera sensor network. At each camera node, an efficient online multiple instance learning algorithm is integrated with particle filter for camera's image plane tracking. To improve the tracking accuracy, each camera node shares its particle states with others and fuses multi-camera information locally. In particular, particle weights are updated according to the fused information. The effectiveness of the proposed algorithm is demonstrated on human tracking in challenging environments.