Visual tracking using sequential importance sampling with a state partition technique

Sequential importance sampling (SIS), also known as particle filtering, has drawn increasing attention recently due to its superior performance in nonlinear and non-Gaussian dynamic problems. In the SIS framework, estimation accuracy depends strongly on the choice of proposal distribution. In this paper we propose a novel SIS algorithm called PF-SP-PEKF that is based on a state partition technique and a parallel bank of extended Kalman filters designed to improve the accuracy of the proposal distribution. Our results show that this new approach yields a significantly improved estimate of the state, enabling the new particle filter to effectively track human subjects in a video sequence where the standard condensation filter fails to maintain track lock. Moreover, because of the improved proposal distribution, the new filter can achieve a given level of performance using fewer particles than its conventional SIS counterparts.