HoG based real-time multi-target tracking in Bayesian framework

Multi-target tracking is one of the most challenging tasks in computer vision. Several complex techniques have been proposed in the literature to tackle the problem. The main idea of such approaches is to find an optimal set of trajectories within a temporal window. The performance of such approaches are fairly good but their computational complexity is too high making them unpractical. In this paper, we propose a novel tracking-by-detection approach in a Bayesian filtering framework. The appearance of a target is modeled through HoG descriptor and the critical problem of target association is solved through combinatorial optimization. It is a simple yet very efficient approach and experimental results show that it achieves state-of-the-art performance in real time.