CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Feature Distilled Tracking
Zhu Guibo1; Jinqiao Wang1,2; Peisong Wang1,2; Yi Wu3,4; Hanqing Lu1,2
Source PublicationIEEE Transaction on Cybernetics
Feature extraction and representation is one of the most important components for fast, accurate, and robust visual tracking. Very deep convolutional neural networks (CNNs) provide effective tools for feature extraction with good generalization ability. However, extracting features using very deep CNN models needs high performance hardware due to its large computation complexity, which prohibits its extensions in real-time applications. To alleviate this problem, we aim at obtaining small and fast-to-execute shallow models based on model compression for visual tracking. Specifically, we propose a small feature distilled network (FDN) for tracking by imitating the intermediate representations of a much deeper network. The FDN extracts rich visual features with higher speed than the original deeper network. To further speed-up, we introduce a shift-and-stitch method to reduce the arithmetic operations, while preserving the spatial resolution of the distilled feature maps unchanged. Finally, a scale adaptive discriminative correlation filter is learned on the distilled feature for visual tracking to handle scale variation of the target. Comprehensive experimental results on object tracking benchmark datasets show that the proposed approach achieves 5x speed-up with competitive performance to the state-of-the-art deep trackers.
KeywordCorrelation Filter Model Compression Visual Tracking
Subject AreaComputer Science, Artificial Intelligence, Cybernetics
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China under Grant 61702510, Grant 61773375, Grant 61370036, Grant 61772277, and Grant 61772527.
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Document Type期刊论文
Corresponding AuthorJinqiao Wang
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Nanjing Audit University
4.Indiana University School of Medicine
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhu Guibo,Jinqiao Wang,Peisong Wang,et al. Feature Distilled Tracking[J]. IEEE Transaction on Cybernetics,2017(0):0.
APA Zhu Guibo,Jinqiao Wang,Peisong Wang,Yi Wu,&Hanqing Lu.(2017).Feature Distilled Tracking.IEEE Transaction on Cybernetics(0),0.
MLA Zhu Guibo,et al."Feature Distilled Tracking".IEEE Transaction on Cybernetics .0(2017):0.
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