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