An Efficient Multi-object Tracking Method Using Multiple Particle Filters

Multiple objects tracking is an important and challenging issue, because of difficulties caused by variable number of objects and interaction of objects. In this paper, we present a distributed tracking approach based on Bayesian framework to avoid huge computational expenses involved in sampling from a joint state space. Single-object trackers easily suffer from false identities of objects after severe occlusions because of hidden first-order Markov hypotheses. To solve the problem, we define a transition matrix between consecutive frames to denote the occurrences and probabilities of dynamic events, such as continuation, appearance, disappearance, interaction and split associating current object detections and previous tracking results. Analyzing transition probabilities combined with position, direction and appearance, we can infer depth ordering of occlusions.The transition matrix is able to effectively guide multiple single-object particle filters to predict and update the state of objects. The simulations demonstrate that the proposed approach can initialize automatically and track varying number of objects with occlusions.