Scalable Particle Filter Framework for Visual Tracking
نویسندگان
چکیده
The GPU, as a data-parallel processor architecture, needs an independent-data (parallelizable) processing model in order to provide the best performance. One of the most popular methods for visual tracking is the Particle Filter (PF) algorithm. The PF algorithm enables the modeling of a stochastic process with an arbitrary probability density function by approximating it numerically with a weighted set of samples called particles. Those particles are independent one from each other, and thus, PF becomes ideal for the GPU computation model.
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تاریخ انتشار 2006