Distributed particle filter tracking with online multiple instance learning in a camera sensor network

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 used to model object´s appearance. This 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. Then, appearance model is updated with the re-weighted particles. The effectiveness of the proposed algorithm is demonstrated on human tracking in challenging environments.