Graph-based sequential particle filtering in lossy networks: Single and multiple collaborative cameras

This paper presents a novel approach of multiple target tracking from multiple collaborative cameras. Firstly, particle filtering for conditional density propagation on graphs to address missing frames from one view is introduced. The Markov Properties and Separation Theorem are used to derive an exact solution for estimation on graphs with missing frames. Furthermore, a distributed multiple target tracking solution from multiple cameras is proposed by using collaborative particle filters. With epipolar geometry constraint, camera collaboration message is delivered between different views by particles. Results demonstrate that our system can deal with missing frames in the presence of occlusions.