Iterated unscented Kalman particle filter for visual tracking

Visual tracking is a challenging task due to its large motion uncertainties. Particle filter has grown to be a standard framework for visual tracking. In this paper, we proposed to design better proposal distribution using a new version of unscented Kalman filter-the iterated unscented Kalman filter (IUKF). The IUKF makes use of both statistical and analytical linearization techniques in different steps of the filtering process, which makes it a better candidate for designing proposal distribution in particle filter framework. Each particle is updated using iterative manner. Through this process, the algorithm can make better use of the current observation for state estimation. The experimental results have shown that the proposed algorithm outperforms the state-of-the-art tracking algorithms in handling different tracking difficulties. Copyright © 2014 Binary Information Press.