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Particle filter has recently received attention in computer vision applications due to attributes such as its ability to carry multiple hypotheses and its relaxation of the linearity assumption. Its shortcoming is increase in complexity with state dimension. We present kernel particle filter as a variation of particle filter with improved sampling efficiency and performance in visual tracking. Unlike existing methods that use stochastic or deterministic optimization procedures to find the modesdoi:10.1109/icip.2003.1247410 dblp:conf/icip/ChangA03 fatcat:qk4wcvy33rafni4d7ttaohn6j4