In this paper, we proposed a tracking method with dual spatio-temporal context trackers that hold different learning rate during tracking. The tracker with high learning rate could track the target smoothly when the appearance of target changes, while the tracker with low learning rate could percepts the occlusion occurring and continues to track when the target starts to emerge again. To find the target among the candidates from these two trackers, we adopt Normalized Correlation Coefficient (NCC) to evaluate the confidence of each sample. Experimental results show that the proposed algorithm performs robustly against several state-of-the-art tracking methods.
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Shiyan Sun, Hong Zhang, Ding Yuan, "Robust visual tracking with dual spatio-temporal context trackers ," Proc. SPIE 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015), 98170X (9 December 2015);