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A Visual Tracking Algorithm Combining Parallel Network and Dual Attention-Aware Mechanism
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DOI：10.1109/ACCESS.2023.3245526.png)
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摘要
En 中文
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 with dual attention-aware 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. 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.
Keyword:
Transformers
Feature extraction
Convolutional neural networks
Target tracking
Visualization
Boundary conditions
Prediction algorithms
Object detection
Convolution neural network
attention mechanism
boundary detection
object tracking
feature extraction
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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