Spatio-temporal weighted histogram based mean shift for illumination robust target tracking

This paper proposes a simple method to handle illumination variation in a video. The proposed method is based on generative mean shift tracker, which uses energy compaction property of discrete Cosine transform (DCT) to handle illumination variation within and across frames. The proposed method uses spatial and temporal DCT coefficient based approach to assign weights to target and candidate histograms in mean shift. The proposed weighing factor takes care of changes in illumination within a frame i.e., illumination change of the target with respect to background and also across the frames i.e., varying illumination between the consecutive time instances. The algorithm was tested using VOT2015 challenge dataset and also on sequences from OTB and CAVIAR datasets. The proposed method was also tested rigorously for illumination attribute. The qualitative and quantitative evaluation process of the proposed method was twofold. First, the tracker was compared with existing DCT coefficient based method and showed improved results. Secondly, the proposed algorithm was compared with other state of the art trackers. The results show that the proposed algorithm outperformed some state-of-the-art trackers while with others it showed comparable performance.