Likelihood adjustment among multiple targets for particle dependent tracking in particle filters

A problem arising at multiple target tracking with particle filters typically in vision has been claimed and a likelihood adjustment method has been proposed. First, classify tracking methods by particle filters into two categories, detection first tracking and particle dependent tracking. Then this research focus on the particle dependent tracking. It involves the problem in case of multiple target tracking that difference of likelihood among target leads to unintended convergence of particles to one target. This is a phenomenon in particle filters that particles prefer easier target having large likelihood value than the difficult target to track having small likelihood value. To overcome this problem, the author proposes to adjust the likelihood among the targets by taking difference in log-likelihood to local maximum of them in each target. Performance of the proposed method has been shown in visual target tracking experiment based on color region.