Title:
Kernel_particle_filter_for_visual_tracking
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Description: 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
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Kernel_particle_filter_for_visual_tracking.pdf
