A comparison of multi hypothesis kalman filter and particle filter for multi-target tracking

Visual tracking of multiple targets is a key step in surveillance scenarios, far from being solved due to its intrinsic ill-posed nature. In this paper, a comparison of Multi-Hypothesis Kalman Filter and Particle Filter-based tracking is presented. Both methods receive input from a novel online background subtraction algorithm. The aim of this work is to highlight advantages and disadvantages of such tracking techniques. Results are performed using public challenging data set (PETS 2009), in order to evaluate the approaches on significant benchmark data.