Visual object tracking is a hot topic in recent years.In the meanwhile,Siamese networks have attracted extensive attention in this field because of its balanced precision and speed.However,most of the Siamese network ...Visual object tracking is a hot topic in recent years.In the meanwhile,Siamese networks have attracted extensive attention in this field because of its balanced precision and speed.However,most of the Siamese network methods can only distinguish foreground from the non-semantic background.The fine-tuning and retraining of fully-convolutional Siamese networks for object tracking(SiamFC)can achieve higher precision under interferences,but the tracking accuracy is still not ideal,especially in the environment with more target interferences,dim light,and shadows.In this paper,we propose crisscross attentional Siamese networks for object tracking(SiamCC).To solve the imbalance between foreground and non-semantic background,we use the feature enhancement module of criss-cross attention to greatly improve the accuracy of video object tracking in dim light and shadow environments.Experimental results show that the maximum running speed of SiamCC in the object tracking benchmark dataset is 90 frames/second.In terms of detection accuracy,the accuracy of shadow sequences is greatly improved,especially the accuracy score of sequence HUMAN8 is improved from 0.09 to 0.89 compared with the original SiamFC,and the success rate score is improved from 0.07 to 0.55.展开更多
基金This work was supported in part by the National Natural Science Foundation of China under Grant 62002392,author Y.T,http://www.nsfc.gov.cn/in part by the Natural Science Foundation of Hunan Province(No.2020JJ4140),author Y.T,http://kjt.hunan.gov.cn/+3 种基金and in part by the Natural Science Foundation of Hunan Province(No.2020JJ4141),author X.X,http://kjt.hunan.gov.cn/in part by the Postgraduate Excellent teaching team Project of Hunan Province under Grant[2019]370-133,author J.Q,http://xwb.gov.hnedu.cn/in part by the Postgraduate Scientific Research Innovation Project of Hunan Province under Grant CX20210878,author Z.W,http://jyt.hunan.gov.cn/and in part by Scientific Innovation Fund for Post-graduates of Central South University of Forestry and Technology under Grant CX202102056,author Z.W,https://jwc.csuft.edu.cn/.
文摘Visual object tracking is a hot topic in recent years.In the meanwhile,Siamese networks have attracted extensive attention in this field because of its balanced precision and speed.However,most of the Siamese network methods can only distinguish foreground from the non-semantic background.The fine-tuning and retraining of fully-convolutional Siamese networks for object tracking(SiamFC)can achieve higher precision under interferences,but the tracking accuracy is still not ideal,especially in the environment with more target interferences,dim light,and shadows.In this paper,we propose crisscross attentional Siamese networks for object tracking(SiamCC).To solve the imbalance between foreground and non-semantic background,we use the feature enhancement module of criss-cross attention to greatly improve the accuracy of video object tracking in dim light and shadow environments.Experimental results show that the maximum running speed of SiamCC in the object tracking benchmark dataset is 90 frames/second.In terms of detection accuracy,the accuracy of shadow sequences is greatly improved,especially the accuracy score of sequence HUMAN8 is improved from 0.09 to 0.89 compared with the original SiamFC,and the success rate score is improved from 0.07 to 0.55.
