Corlor Names based scale-adaptive object tracking

In this paper, we address the problem of tracking drift and failure due to background clustering, illumination and scale changes. To resolve these problems, we propose an efficient model that project original RGB color space to a more robust color space-Color Names feature space. Furthermore, we represent objects by background weighted histograms, and thus suppress the similar background around. Moreover, we adapt a two-step tuning way to estimate the scale by coarse tuning with gradient ascent and fine tuning with constrained items. Back-forward scale check is used to ensure the precision of scale estimation. The results show that our approach is robust to background clustering, illumination variation, partial occlusion, and viewpoint changes.