Abstract
Visual tracking is defined as a template-matching task in current Siamese approaches. The tracker needs to locate the target by matching the template with the search area in each frame. Most current Siamese methods either do not use an update strategy or use a linear update method with a fixed learning rate. Neither of the above two strategies allows the target template to dynamically adapt to frequent and dramatic changes in appearance. To solve this problem, we propose a template update model based on the attention mechanism. Our model updates the template in a nonlinear manner. It can fully explore the weight relationship of various features in the template, so that the template can pay more attention to features that are more beneficial to determine the target in different situations. In addition, by adding an adjustment block, the error and invalid information in the old template can be removed before updating. Extensive experiments on several datasets demonstrated the effectiveness of our update model. We used SiamFC++ as our basic tracker and achieved state-of-the-art performance by adding our model. Moreover, our model has lightweight structure, and thus it can be easily applied to most Siamese trackers with minimal computational cost.
F. Jia—Graduate student.
This work is supported by National Natural Science Foundation of China (No. 61802337).
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Jia, F., Tang, Z., Gao, Y. (2021). Attention Template Update Model for Siamese Tracker. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13019. Springer, Cham. https://doi.org/10.1007/978-3-030-88004-0_19
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