Abstract：In order to improve the tracking accuracy of fast online target tracking and segmentation algorithm, a dynamic weighted siamese network tracking algorithm was proposed. First, the template features extracted from the initial frame and the template features extracted from each frame were learned and fused to improve the generalization ability of the tracker. Second, in the process of obtaining the target mask by the mask branch, the features were fused in a weighting method, so as to reduce the interference caused by redundant features and improve the tracking accuracy. The algorithm was evaluated on the VOT2016 and VOT2018 datasets. The results show that the proposed algorithm has the expected average overlap rate of 0.450 and 0.390 respectively, the accuracy of 0.649 and 0.618 respectively, and the robustness of 0.205 and 0.267 respectively, all of which are higher than those of baseline algorithm. The tracking speed of the proposed algorithm is 34 frame/s, which meets the requirements of real-time tracking. The proposed algorithm effectively improves the tracking accuracy, and completes the tracking task well in a complex tracking environment.
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2020, Vol. 40 
