NDLI: Robust visual tracking via self-similarity learning

Content Provider	IET Digital Library
Author	Song, Huihui Zheng, Yuhui Zhang, Kaihua
Abstract	Self-similarity is an attractive image property that has been successfully applied to object recognition due to its robustness to severe target appearance variations. However, less attention has been paid to explore self-similarity for visual tracking, mainly because it is difficult to learn self-similarity information between different features suitable for visual tracking. To address this issue, a simple, yet effective approach is presented to learn self-similarity information among the local features extracted from the different regions of the target. The target is first divided into some non-overlapping regions, in which each region is described by the histogram of gradient (HOG) features. Then, an explicit polynomial kernel feature map is constructed, which is capable of characterising the self-similarity information among all the local regions in the targets. Finally, based on the feature maps, a linear support vector machine (SVM) is learnt via an online dual coordinate descent method that offers fast convergence guarantee. Experiments on a large tracking benchmark dataset with 50 sequences demonstrate the superiority of the proposed method over state-of-the-art methods.
Starting Page	20
Ending Page	22
Page Count	3
ISSN	00135194
Volume Number	53
e-ISSN	1350911X
Issue Number	Issue 1, Jan (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/53/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2016.3011
Journal	Electronics Letters
Publisher Date	2016-11-22
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Computer Vision And Image Processing Technique Explicit Polynomial Kernel Feature Map Feature Extraction HOG Feature Image Property Image Recognition Knowledge Engineering Technique Learning in AI Linear SVM Local Feature Extraction Object Recognition Object Tracking Online Dual Coordinate Descent Method Polynomial Robust Visual Tracking Self-similarity Information Learning Support Vector Machine Tracking Benchmark Dataset
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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