8 October 2015 Mean-shift tracking algorithm based on adaptive fusion of multi-feature
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Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 96751L (2015) https://doi.org/10.1117/12.2199504
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China
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.
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Kai Yang, Kai Yang, Yanghui Xiao, Yanghui Xiao, Ende Wang, Ende Wang, Junhui Feng, Junhui Feng, } "Mean-shift tracking algorithm based on adaptive fusion of multi-feature", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96751L (8 October 2015); doi: 10.1117/12.2199504; https://doi.org/10.1117/12.2199504
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