Kernel particle filter: iterative sampling for efficient visual tracking

Cheng Chang, R. Ansari
Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429)  
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 modes
more » ... s to find the modes in a likelihood function, we redistribute particles by invoking kernel-based representation of densities and introducing mean shift as an iterative modeseeking procedure, in which particles move towards dominant modes while still maintaining as fair samples from the posterior. Experiments on face and limb tracking show that the algorithm is superior to conventional particle filter in handling weak dynamic models and occlusions with 60% fewer particles in 3-9 dimensional spaces.
doi:10.1109/icip.2003.1247410 dblp:conf/icip/ChangA03 fatcat:qk4wcvy33rafni4d7ttaohn6j4