Tracking highly correlated targets through statistical multiplexing

We consider a special problem of multi-target tracking, where a group of targets are highly correlated, usually demonstrating a common motion pattern with individual variations. We focus on the task of searching and provide a statistical framework of embedding the correlation among targets and the most recent observations into sampling, where the correlation is learned dynamically from the previous tracking results. Proposal distribution is updated during the sampling process fused with the motion prior and observation information. In this way, the observation of a single target is multiplexed statistically through mutual correlation among the multiple targets, and the correlation serves as both a prior information to improve the efficiency and a constraint to prevent trackers from drifting. Extensive experiments on tracking both naturally correlated and environment-constrained targets demonstrate superior and promising robust results with low complexity.