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Accession number: 20140117152906 Title: A VSS algorithm based on multiple features for object tracking Authors: Xu, Bin1 ; Shen, Xiaoju2 ; Ding, Feiji1 Author affiliation: 1 Department of Mechanical-Electrical Engineering, North China Institute of Science and Technology, Sanhe, 065201, China 2 Department of Management, North China Institute of Science and Technology, Sanhe, 065201, China Source title: Journal of Software Abbreviated source title: J. Softw. Volume: 8 Issue: 12 Issue date: 2013 Publication year: 2013 Pages: 3029-3034 Language: English ISSN: 1796217X Document type: Journal article (JA) Publisher: Academy Publisher, P.O.Box 40,, OULU, 90571, Finland Abstract: A variable search space (VSS) approach according to the color feature combined with point feature for object tracking is presented. Mean shift is a wellestablished and fundamental algorithm that works on the basis of color probability distributions, and is robust to given color targets. As it solely depends upon back projected probabilities, it may miss the targets because of illumination and noise. To overcome the flaw, we proposes VSS algorithm based on the color and robust feature of the detected object. The proposed algorithm can solve the problem that the color of the detected object is similar to the background, and achieve better real-time tracking due to change the search window's size. Experimental work demonstrates that the presented method is robust and computationally effective. © 2013 Academy Publisher. Number of references: 11 Main heading: Algorithms Controlled terms: Color - Probability distributions - Target tracking Uncontrolled terms: Color probability distributions - Mean shift - Multiple features - Object Tracking - Real time tracking - Scale invariant feature transforms - Search spaces - Search windows Classification code: 716.2 Radar Systems and Equipment - 723 Computer Software, Data Handling and Applications - 741.1 Light/Optics - 921 Mathematics - 922.1 Probability Theory DOI: 10.4304/jsw.8.12.3029-3034 Database: Compendex Compilation and indexing terms, © 2013 Elsevier Inc. |
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