Human Tracking Based on Particle Filter in Outdoor Scene
Muroi Mototsugu, Zen Heitoh · 2007
In this paper, we propose the object tracking method based on color histograms and particle filtering. Particle filtering is a time series filter for estimating a state using probabilistic approach. Unlike deterministic approach such as template matching algorithm, it is more robust to occlusion or clutter because of its having many hypotheses. Moreover, color histograms are robust to partial occlusion, scale invariant and computational efficient. However, histogram has no spatial information. To solve this, multi-part histogram which has color histograms divided into sub-regions has been proposed in the past. However, multi-part histogram also has a disadvantage of no working in tracking an object having plain color. Therefore, we propose the adaptive target color representation