A visual tracking algorithm combining parallel network and dual attention-aware mechanism

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Ge, Haibo, Wang, Shuxian, Huang, Chaofeng, An, Yu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Attention mechanism; Boundary detection; Computer networks; Convolution; Convolution neural network; Datasets; Feature extraction; Modules; Object tracking; Optical tracking; Performance evaluation; Prediction algorithms; Semantics; Target tracking; Training; Transformers; Visualization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3245526

In order to solve the problems of semantic loss and inaccurate boundary detection in the process of object tracking, a visual tracking algorithm combining parallel structure and dual attention-attention mechanism is proposed in this paper. As backbone network, parallel structure is composed of Convolutional neural network and Attention Cooperative(CAC) processing module, which is used for feature extraction. Because this structure can capture the local and global information of the target at the same time, it can solve the problem of semantic information loss. Dual Attention-aware Network(DAN) is used for feature enhancement, which is composed of target-aware attention and boundary-aware attention, and the accuracy of boundary detection is enhanced by feature pyramid structure. Template online updating strategy is used to improve template quality, and an effective score prediction module-Template Elimination Mechanism(TEM) is designed in the CAC processing module to select high quality templates. This kind of object tracking algorithm which combines local and global information is called TrackCAC. The evaluation results on different datasets show that the algorithm can maintain high tracking precision and success in different scenarios. It shows good robustness and accuracy in the performance evaluation results on VOT datasets.