中圖分類號： TP391
文獻標識碼： A
DOI： 10.19358/j.issn.2096-5133.2022.06.009
引用格式： 林志雄，吳麗君，陳志聰. 基于注意力機制的無監督單目標跟蹤算法[J].信息技術與網絡安全，2022，41(6)：50-56.

Unsupervised single target tracking algorithm based on attention mechanism

Abstract： In order to improve target tracking accuracy, this paper designs an unsupervised single target tracking algorithm based on attention mechanism. The algorithm uses the DCFNet network as the basic network to achieve unsupervised tracking through forward tracking and backward verification. In order to combine the context information, a feature fusion method is introduced, and the features extracted by each layer of the DCFNet network are adjusted for resolution by bilinear pooling for feature fusion; in order to pay attention to the relationship between different feature channels, a channel attention mechanism SENet module is introduced; a reverse frame-by-frame verification method is designed, and the first frame is predicted based on the reverse verification of the intermediate frame, thereby reducing the error of the discriminant position. The test results on the public dataset OTB-2015 show that the AUC score of this algorithm is 60.6% and the speed is 61FPS. Compared with the unsupervised single-target tracking UDT algorithm, the designed algorithm achieves better target tracking performance.

Key words : target tracking；unsupervised learning；feature fusion；attention mechanism