Cross-attention Guided Siamese Network Object Tracking Algorithm

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.

Keywords

• visual object tracking|siamese network|similar object distractor|cross-attention module|anchor-free regression