Tracking a group of highly correlated targets

Under the probabilistic framework, we consider the problem of tracking a group of highly correlated targets and propose to embed the correlation into the sampling procedure, where the correlation serves as both a prior information to improve the efficiency and a constraint to prevent trackers from confusion or drifting. Experiments under different settings demonstrate promising results in robustness with linear complexity.