Abstract:

In the process of target tracking, the occlusion of the target reduces the performance of the tracker, resulting in the loss of the target. To solve this problem, this paper proposes an adaptive anti-occlusion target tracking algorithm based on LCT+ kernel correlation filter. The algorithm is improved on the basis of LCT+ kernel correlation filtering algorithm, which proposes a strategy of using two trackers to adaptively track the target. According to the output response value of the two trackers, the optimal tracker is selected to track the target. In addition, a strategy for re-detecting targets by using support vector machines is proposed. The detection range is adaptively adjusted according to the number of frames lost of the target. Finally, the predicted target is verified by using color histogram matching. Compared with the original algorithm, the algorithm in this paper adopts a dual tracker mechanism to adaptively track the target and a support vector machine mechanism to adaptively re-detect the target, which effectively avoids the loss of the target. The proposed algorithm is validated in two large benchmark data sets of OTB50 and OTB100. The results indicate that the proposed algorithm is superior to some mainstream algorithms in the evaluation indexes of distance precision and success rate. In terms of target anti-occlusion, it has higher accuracy and strong robustness.

Key words: target tracking, kernel correlation filtering, adaptive anti-occlusion, dual tracker, Support Vector Machine（SVM）