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Proceedings Paper

Mean-shift tracking algorithm based on adaptive fusion of multi-feature
Author(s): Kai Yang; Yanghui Xiao; Ende Wang; Junhui Feng
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Paper Abstract

The classic mean-shift tracking algorithm has achieved success in the field of computer vision because of its speediness and efficiency. However, classic mean-shift tracking algorithm would fail to track in some complicated conditions such as some parts of the target are occluded, little color difference between the target and background exists, or sudden change of illumination and so on. In order to solve the problems, an improved algorithm is proposed based on the mean-shift tracking algorithm and adaptive fusion of features. Color, edges and corners of the target are used to describe the target in the feature space, and a method for measuring the discrimination of various features is presented to make feature selection adaptive. Then the improved mean-shift tracking algorithm is introduced based on the fusion of various features. For the purpose of solving the problem that mean-shift tracking algorithm with the single color feature is vulnerable to sudden change of illumination, we eliminate the effects by the fusion of affine illumination model and color feature space which ensures the correctness and stability of target tracking in that condition. Using a group of videos to test the proposed algorithm, the results show that the tracking correctness and stability of this algorithm are better than the mean-shift tracking algorithm with single feature space. Furthermore the proposed algorithm is more robust than the classic algorithm in the conditions of occlusion, target similar with background or illumination change.

Paper Details

Date Published: 8 October 2015
PDF: 6 pages
Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96751L (8 October 2015); doi: 10.1117/12.2199504
Show Author Affiliations
Kai Yang, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Key Lab. of Opto-Electronic Information Processing (China)
Yanghui Xiao, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Ende Wang, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Key Lab. of Opto-Electronic Information Processing (China)
Junhui Feng, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Key Lab. of Opto-Electronic Information Processing (China)


Published in SPIE Proceedings Vol. 9675:
AOPC 2015: Image Processing and Analysis
Chunhua Shen; Weiping Yang; Honghai Liu, Editor(s)

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