Improvement of mean shift tracker for tracking of target with variable photometric pattern

The mean shift algorithm is one of the popular methods in visual tracking for non-rigid moving targets. Basically، it is able to locate repeatedly the central mode of a desirable target. Object representation in mean shift algorithm is based on its feature histogram within a non-oriented individual kernel mask. Truly، adjusting of the kernel scale is the most critical challenge in this method. Up to now، no methods are presented that can perfectly as well as efficiently adjust and adapt the kernel scale during track when a target is resized. Another problem of mean shift tracking algorithm will be encountered whenever photometric properties of target texture changes. In order to solve these problems، this paper presents a modified mean shift tracking algorithm that is used a robust adaptive sizing technique. It can also cope with photometric changes of target template by adapting of its model in every frame of image sequence. In our proposed method، at first، the target window is adaptively resized with respect to spatio-temporal gradient powers of its pixel intensities in current frame and then mean shift algorithm is consequently applied to the resulted sizing window. Compared to standard mean shift algorithm، experimental results show that our proposed method، not only reduces center location errors of target، but also efficiently track it in the presence of changing illumination.