Open AccessA Siamese Network Tracking Algorithm Based on Hierarchical Attention Mechanism
Author(s) -
Hu Zhang,
Dong Hu,
Yingcan Qiu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1828/1/012044
Subject(s) - computer science , adaptability , clutter , feature (linguistics) , artificial intelligence , tracking (education) , algorithm , pattern recognition (psychology) , adaboost , reliability (semiconductor) , process (computing) , layer (electronics) , mechanism (biology) , power (physics) , support vector machine , psychology , ecology , telecommunications , radar , linguistics , philosophy , pedagogy , physics , organic chemistry , chemistry , epistemology , quantum mechanics , biology , operating system
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.