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Proceedings Paper

The performance comparison of different feature to kernelized correlation filter tracker
Author(s): Mengna Liu; Chen Diao; Xu Cheng; Shengyong Chen
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Paper Abstract

Feature extraction plays an important role in the tracking process. However, most attention is paid to the performance of the tracker without considering the sensitivity of feature to the environment. In this paper, the tracker based on the Kernelized Correlation Filter (KCF) is chosen to compare the tracking performance of different features such as the grayscale, the Histogram of Oriented Gradients (HOG) and the Color Names on scenarios with different attributes. The tracking accuracies corresponding to different features on sequences with various attributes are compared and validated through the OTB-100 dataset and the ALOV300++ dataset. The results show that the HOG gets the best tracking performance on image sequences with attributes such as background clutters, illumination variation, out-of-plane rotation, occlusion, deformation compared with grayscale and the Color Names. And the grayscale gets the best performance for motion blur, in-plane rotation. The Color Names obtains the best result with scale variation. And the reasons for performance differences between the three features are analyzed. It can be concluded that the accuracy of a tracker can be improved by choosing a proper feature according to the attributes of scenes.

Paper Details

Date Published: 6 May 2019
PDF: 7 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110694A (6 May 2019); doi: 10.1117/12.2524233
Show Author Affiliations
Mengna Liu, Tianjin Univ. of Technology (China)
Chen Diao, Tianjin Univ. of Technology (China)
Xu Cheng, Tianjin Univ. of Technology (China)
Shengyong Chen, Tianjin Univ. of Technology (China)

Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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