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DCFNet:Discriminant Correlation Filters Network for Visual Tracking

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CNN(convolutional neural network)based real time trackers usually do not carry out online network up-date 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 convolution-al 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 predic-tions 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 embed-ding 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.

correlation filterconvolutional neural network(CNN)visual tracking

胡卫明、王强、高晋、李兵、Stephen Maybank

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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.

National Key Research and Development Program of ChinaNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaBeijing Natural Science Foundation

2020AAA-01058022020AAA0105800620360116219278261721004U2033210L223003

2024

10.1007/s11390-023-3788-3

计算机科学技术学报(英文版)
中国计算机学会

计算机科学技术学报(英文版)

CSTPCD
影响因子：0.432
ISSN：1000-9000
年,卷(期)：2024.39(3)