Cross-attention Guided Siamese Network Object Tracking Algorithm
Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (3): 163-169.doi: 10.11896/jsjkx.210300066

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Cross-attention Guided Siamese Network Object Tracking Algorithm

ZHAO Yue, YU Zhi-bin, LI Yong-chun   

  1. College of Electronic Engineering,Southwest Jiaotong University,Chengdu 611756,China
  • Received:2021-03-08 Revised:2021-04-13 Online:2022-03-15 Published:2022-03-15
  • About author:ZHAO Yue,born in 1995,postgraduate.His main research interests include artificial intelligence,pattern recognition and computer vision.
    YU Zhi-bin,born in 1977,Ph.D,asso-ciate professor,is a member of China Computer Federation.His main research interests include artificial intelligence,pattern recognition and signal processing.
  • Supported by:
    National Defense Pre-Research Foundation of China(61403120304).

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Abstract: Most traditional Siamese trackers cannot perform robust when facing the similar object,deformation,background clutters and other challenges.Accordingly,a cross-attention guided Siamese network (called SiamCAN) is proposed to solve the above problem in this paper.Firstly,different layers of ResNet50 are used to get various revolutions of object feature and a cross-attention module is designed to bridge the information flow between search branch and template branch.After that,each feature from different layers of backbone is sent to CNNs to update parameters and combined with each other,in classification network and regression network.Finally,the predicted location and target size are calculated according to the max response on response map.Simulation experimental results on the UAV123 tracking dataset show that the tracking precision is improved by 1.7% and the tracking accuracy is improved by 0.7%,compared to the mainstream algorithm SiamBAN.Moreover,on the VOT2018 benchmark,the EAO of our method outperforms 2.5 than the mainstream algorithm SiamRPN++,and the tracking speed of our method maintains 35FPS.

Key words: Anchor-free regression, Cross-attention module, Siamese network, Similar object distractor, Visual object tracking

CLC Number: 

  • TP391
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