Multiple hypothesis tracking in cluttered condition

Multiple hypothesis tracking (MHT) is a preferred technique for solving the data association problem in modern multiple targets tracking systems. However its computational cost is generally considered prohibitive for tracking numerous objects in cluttered environments due to numerous targets and spurious measurements. We present in this paper a new MHT formulation in which target perceivability is modeled whereby automatic early track termination and false measurements exclusion reduce the problem complexity and improve the method robustness to clutter. Moreover we propose a MHT implementation exploiting the tree structure of the potential tracks to take full advantages of parallel computing technologies. We provide experimental results showing that both the track model and algorithmic design make the algorithm fast and robust even in highly complex situations such as tracking numerous particles in fluorescent microscopy images.