DCFNet:Discriminant Correlation Filters Network for Visual Tracking  

作　　者：胡卫明 王强 高晋 李兵 Stephen Maybank Wei-Ming Hu;Qiang Wang;Jin Gao;Bing Li;Stephen Maybank(State Key Laboratory of Multimodal Artificial Intelligence Systems,Institute of Automation,Chinese Academy of Sciences Beijing 100190,China;Department of Computer Science and Information Systems,Birkbeck College,London WC1E 7HX,U.K.)

机构地区：[1]State Key Laboratory of Multimodal Artificial Intelligence Systems,Institute of Automation,Chinese Academy of Sciences Beijing 100190,China [2]Department of Computer Science and Information Systems,Birkbeck College,London WC1E 7HX,U.K.

出　　处：《Journal of Computer Science & Technology》2024年第3期691-714,共24页计算机科学技术学报（英文版）

基　　金：supported by the National Key Research and Development Program of China under Grant Nos.2020AAA0105802 and 2020AAA0105800;the National Natural Science Foundation of China under Grant Nos.62036011,62192782,61721004,and U2033210;the Beijing Natural Science Foundation under Grant No.L223003.

摘　　要：CNN(convolutional neural network)based real time trackers usually do not carry out online network update in order to maintain rapid tracking speed.This inevitably influences the adaptability to changes in object appearance.Correlation filter based trackers can update the model parameters online in real time.In this paper,we present an end-to-end lightweight network architecture,namely Discriminant Correlation Filter Network(DCFNet).A differentiable DCF(discriminant correlation filter)layer is incorporated into a Siamese network architecture in order to learn the convolutional features and the correlation filter simultaneously.The correlation filter can be efficiently updated online.In previous work,we introduced a joint scale-position space to the DCFNet,forming a scale DCFNet which carries out the predictions of object scale and position simultaneously.We combine the scale DCFNet with the convolutional-deconvolutional network,learning both the high-level embedding space representations and the low-level fine-grained representations for images.The adaptability of the fine-grained correlation analysis and the generalization capability of the semantic embedding are complementary for visual tracking.The back-propagation is derived in the Fourier frequency domain throughout the entire work,preserving the efficiency of the DCF.Extensive evaluations on the OTB(Object Tracking Benchmark)and VOT(Visual Object Tracking Challenge)datasets demonstrate that the proposed trackers have fast speeds,while maintaining tracking accuracy.

关 键 词：correlation filter convolutional neural network(CNN) visual tracking 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]