Kernel particle filter for visual tracking

A new particle filter-the Kernel Particle Filter (KPF)-is proposed for visual tracking in image sequences. The KPF invokes kernels to form a continuous estimate of the posterior density function. Particles are allocated based on the gradient information estimated from the kernel density estimate of the posterior. Results from simulations and experiments with real video data show the improved performance of the proposed algorithm when compared with that of the standard particle filter. The superior performance is evident in scenarios of small system noise or weak dynamic models where the standard particle filter usually fails.