Object Tracking Algorithm Based on Correlation-Transformer Dual Feature Fusion
The current algorithms mainly use one of the cross-correlation operation and Transformer methods to design feature fusion network,which ignores the complementary advantages between the two methods,and is prone to lose seman-tic information and fall into local optimum.In order to solve the above problems,an object tracking algorithm based on correlation-Transformer dual feature fusion is designed.The improved cross-correlation operation and Transformer methods are used to fuse template and search area features respectively.The advantages of these two fusion methods are complemen-tary,so that template and search area features can fully interact.In order to achieve effective enhancement and full fusion of features,the similarity matrix is introduced into cross-correlation operation to enhance features associated with target in current frame in template and search area,so that the matching process of cross-correlation operation is more accurate.The object tracking algorithm includes a backbone network based on S win-Transformer,a cross-correlation and Transformer dual fusion module,as well as a prediction branch.The proposed algorithm achieves robust results on TrackingNet,LaSOT,NFS,UAV123 and OTB2015 datasets,with success rate of 81.8％,65.7％,66.2％,69.4％and 69.8％,respectively,and an average tracking speed of 40 frame/s.
万方数据
维普
