NDLI: Robust visual tracking with adaptive initial configuration and likelihood landscape analysis

Content Provider	IET Digital Library
Author	Kim, Guisik Kwon, Junseok
Abstract	Here, the authors propose a novel tracking algorithm that can automatically modify the initial configuration of a target to improve the tracking accuracy in subsequent frames. To achieve this goal, the authors’ method analyses the likelihood landscape (LL) for the image patch described by the initial configuration. A good configuration has a unimodal distribution with a steep shape in the LL. Using the LL analysis, the authors’ method improves the initial configuration, resulting in more accurate tracking results. The authors improve the conventional LL analysis based on two ideas. First, the authors’ method analyses the LL in the RGB space rather than the grey space. Second, the method introduces an additional criterion for a good configuration: a high likelihood value at the mode. The authors further enhance their method through post-processing of the visual tracking results at each frame, where the estimated bounding boxes are modified by the LL analysis. The experimental results demonstrate that the authors’ advanced LL analysis helps improve the tracking accuracy of several baseline trackers on a visual tracking benchmark data set. In addition, the authors’ simple post-processing technique significantly enhances the visual tracking performance in terms of precision and success rate.
Starting Page	1
Ending Page	7
Page Count	7
ISSN	17519632
Volume Number	13
e-ISSN	17519640
Issue Number	Issue 1, Feb (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/13/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5359
Journal	IET Computer Vision
Publisher Date	2018-09-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Initial Configuration Computer Vision And Image Processing Technique Conventional LL Analysis High Likelihood Value Image Colour Analysis Likelihood Landscape Analysis Object Detection Object Tracking Optical, Image And Video Signal Processing RGB Space Robust Visual Tracking Target Tracking Tracking Accuracy Visual Tracking Benchmark Data Visual Tracking Performance
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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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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