Robust color-based tracking

Color as a distinct feature is widely used for object representation and tracking. However, color-based tracking is often influenced by clutter background and illumination variation. This paper presents a robust color-based tracking method, in which robust color feature is extracted for constructing the observation model under the modified particle filter tracking framework. The object is represented by its dominant color, and the weighted histogram with spatial information of the dominant color is used to optimize object models. In the particle filter framework, an extended iterated likelihood weighting scheme is employed to utilize more valuable particles. The experimental results show it is a real-time robust tracker, and it can obtain more than 30 fps with 2.4 G CPU and 512 MRAM.