Sequential particle filtering for conditional density propagation on graphs

In this paper, we develop novel solutions for particle filtering on graphs. An exact solution of particle filtering for conditional density propagation on directed cycle-free graphs is performed by a sequential updating scheme in a predetermined order. We also provide an approximate solution for particle filtering on general graphs by splitting the graphs with cycles into multiple directed cycle-free subgraphs. We utilize the proposed solution for distributed multiple object tracking. Experimental results show the improved performance of our method compared with existing methods for multiple object tracking.