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
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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2023, Vol. 44 
