A Siamese Network Tracking Algorithm Based on Hierarchical Attention Mechanism. Issue 1 (February 2021)

• Record Type: Journal Article
• Authors: Zhang, Hu; Hu, Dong; Qiu, Yingcan
• Is Part Of: Journal of physics. Volume 1828:Issue 1(2021)
• Publication Date: 2021-02
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1828/1/012044
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: A siamese network tracking algorithm based on hierarchical attention mechanism is proposed in this paper. In order to obtain more robust target tracking results, different layer features are fused effectively. In the process of extracting features, attention mechanism is used to recalibrate the feature map, and AdaBoost algorithm is used to weight the target feature map, which improves the reliability of the response map. Besides, the Inception module is also introduced which not only increases the width of the network and the adaptability of the siamese network to the scale, but also reduces the parameters and improves the speed of network training. Experimental results show that this method can effectively solve the impact of background clutter and improve the accuracy of tracking.