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Target Tracking Algorithm Based on Normalized Attention Mechanism with Feature Adaptive Fusion |
ZHANG Li-guo1,2,ZHANG Yu-peng1,2,JIN Mei1,2,ZHANG Sheng1,2,GENG Xing-shuo1,2 |
1. Hebei Key Laboratory of Measurement Technology and Instrument, Yanshan University,
Qinhuangdao, Hebei 066004, China
2.School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract To solve the difficult problems of image deformation and low resolution in fast moving target tracking. A feature-adaptive fusion target tracking algorithm based on a normalised attention mechanism is proposed based on current siamese networks. Firstly, the less obvious weights are suppressed by a lightweight attention mechanism that imposes a weight sparsity penalty on the attention module. While a path enhancement method is proposed, path strengthening is applied to the last four feature layers of the backbone. Secondly, a plug-in template online update method is proposed in order to capture changes in the appearance of the target during online tracking and improve the robustness of the algorithm. Finally, a regression-enhanced classification method is used to complete the tracking of the target. The experimental results show that the proposed algorithm achieves high success rates of 63.3% and 59.5% on two challenging datasets, OTB100,UAV123, respectively. Also, the algorithm has strong robustness when the illumination changes, the image background is complex and the target is rotated in-plane.
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Received: 27 April 2022
Published: 21 September 2023
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Fund:Science and technology Support Program of Science and Technology Research and Development Program of Hebei Province;The central government of Hebei province guides local special projects |
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