Illumination invariant Mean-shift tracking
Gargi Phadke, Rajbabu Velmurgan · 2013
Visual tracking is a critical task in surveillance and activity analysis. One of the major issues in visual target tracking is variations in illumination. In this paper, we propose a novel algorithm based on discrete cosine transform (DCT) to handle illumination variations, since illumination variations are mainly reflected in the low-frequency band. For instance, low illumination in a frame leads to low value DC coefficient as vias versa. We modify DC coefficient to achieve illumination invariance. Average of DC coefficients of particular numbers of neighboring frames of current frame is taken. The correction in DC coefficient is performed using maximum eigen value of the image co-variance matrix over N frames. The videos with corrected illumination are than used to track objects of interest using the Mean shift algorithm. The proposed algorithm is tested on an exhaustive database. The results demonstrate significantly improved tracking.