Parallel particle-PHD filter

The complexity of multi-target tracking grows faster than linearly with the increase of the numbers of objects, thus making the design of real-time trackers a challenging task for scenarios with a large number of targets. The Probability Hypothesis Density (PHD) filter is known to help reducing this complexity. However, this reduction may not suffice in critical situations when the number of targets, dimension of the state vector, clutter conditions and sample rate are high. To address this problem, we propose a parallelization scheme for the particle PHD filter. The proposed scheme exploits the knowledge of mutual interacting targets in the scene to help fragmentation and to reduce the workload of individual processors. We compare the proposed approach with alternative parallelization schemes and discuss its advantages and limitations using the results obtained on two multi-target tracking datasets.