Robust mean shift tracking method under varying illumination

Color can provide an efficient visual cue for tracking based on appearance models. However, the apparent color of an object depends upon the illumination conditions, the viewing geometry and the camera parameters, all of which can vary during tracking and therefore make the tracking based on apparent color models unreliable or even failed. In this paper a mean shift tracking algorithm is proposed based on dynamic corrected fuzzy color histogram, which employs local background information around the target to correct the apparent models and overcomes the sensitive of conventional color histogram to illumination change and noise. The algorithm is tested on several image sequences and the results show that it can smooth the similarity surface and achieve robust and reliable frame-rate tracking under varying illumination conditions.