Probabilistic Detection-based Particle Filter for Multi-target Tracking

In this paper, we present a Probabilistic Detection-based Particle Filter (PD-PF) for tracking a variable number of interacting targets. When the objects do not interact with each other, our method performs like the deterministic detection-base methods. When the objects are in close proximity, the interactions and occlusions are modelled by a mixed proposal constructed by probabilistic detections and information from dynamic models. Specially, prior of detection-reliability minimizes the influence of non-detection or false alarm in the tracking. Moreover, we run independent PD-PF for each target, such that particles are sampled in a small state space, thus our method not only obtains a better approximation of posterior than joint particle filter or independent particle filter when interactions occur, but also has an acceptable computational complexity. Different evaluations demonstrate the validity and efficiency of the proposed method.