NDLI: Optimal colour-based mean shift algorithm for tracking objects

Content Provider	IET Digital Library
Author	An, Xiaowei Kim, Jaedo Han, Youngjoon
Abstract	The mean-shift method is widely used to locate a target object quickly in sequential images. The mean-shift algorithm takes advantage of a colour distribution with a uniform quantisation. However, the quantisation method ignores the close relationship of colour statistics. The uniform distribution also results in a colour histogram with many empty bins, which introduces additional computation cost in the tracking procedure. To reduce the number of these redundant, empty bins, the authors present a new optimal colour-based, mean-shift algorithm for tracking objects. In the proposed method, the optimal colours are extracted by a histogram agglomeration, which clusters three-dimensional (3D) colour histogram bins with the frequency ratios of 3D colour values. After obtaining optimal colours in a RGB colour histogram, the target image is represented by the indices of the optimal colours. The mean-shift algorithm thus creates a confidence map in a candidate image based on the optimal colour histogram in the target image. It then finds the peak of the confidence map near the previous position of an object area. Comparative experiments with the conventional mean-shift method showed that our method has the advantages of decreased processing time and improved tracking accuracy.
Starting Page	235
Ending Page	244
Page Count	10
ISSN	17519632
Volume Number	8
e-ISSN	17519640
Issue Number	Issue 3, Jun (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/8/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2013.0004
Journal	IET Computer Vision
Publisher Date	2013-09-30
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	3D Colour Histogram Bins Colour Distribution Colour Statistics Computation Cost Computer Vision Computer Vision And Image Processing Technique Confidence Map Feature Extraction Histogram Agglomeration Extraction Image Colour Analysis Image Recognition Image Representation Image Sequence Object Target Localization Object Tracking Optimal Colour-based Mean Shift Algorithm Quantisation (signal) RGB Colour Histogram Sequential Image Statistical Distribution Statistics Target Image Representation Uniform Distribution Uniform Quantisation Method
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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