Lightweight Target-Aware Attention Learning Network-Based Target Tracking Method

Yanchun Zhao, Jiapeng Zhang, Rui Duan, Fusheng Li and Huanlong Zhang
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Yanchun Zhao: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Jiapeng Zhang: School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
Rui Duan: School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
Fusheng Li: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Huanlong Zhang: School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China

Mathematics, 2022, vol. 10, issue 13, 1-18

Abstract: Siamese network trackers based on pre-trained depth features have achieved good performance in recent years. However, the pre-trained depth features are trained in advance on large-scale datasets, which contain feature information of a large number of objects. There may be a pair of interference and redundant information for a single tracking target. To learn a more accurate target feature information, this paper proposes a lightweight target-aware attention learning network to learn the most effective channel features of the target online. The lightweight network uses a designed attention learning loss function to learn a series of channel features with weights online with no complex parameters. Compared with the pre-trained features, the channel features with weights can represent the target more accurately. Finally, the lightweight target-aware attention learning network is unified into a Siamese tracking network framework to implement target tracking effectively. Experiments on several datasets demonstrate that the tracker proposed in this paper has good performance.

Keywords: target features; siamese trackers; lightweight network; target tracking (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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