| ZHOU Tong-xue,ZENG Dong-dong,ZHU Ming,Arjan Kuijper.A template consensus method for visual tracking[J].Optoelectronics Letters,2019,15(1):70-74 |
| A template consensus method for visual tracking |
| Author Name | Affiliation | | ZHOU Tong-xue | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, Chi-na
The University of the Chinese Academy of Sciences, Beijing 100049, China
Department of Graphic Interactive System and Mathematical and Applied Visual Computing, Fraunhofer Institute for Computer Graphics Research IGD, Darmstadt 64283, Germany | | ZENG Dong-dong | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, Chi-na
The University of the Chinese Academy of Sciences, Beijing 100049, China
Department of Graphic Interactive System and Mathematical and Applied Visual Computing, Fraunhofer Institute for Computer Graphics Research IGD, Darmstadt 64283, Germany | | ZHU Ming | Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, Chi-na
The University of the Chinese Academy of Sciences, Beijing 100049, China | | Arjan Kuijper | Department of Graphic Interactive System and Mathematical and Applied Visual Computing, Fraunhofer Institute for Computer Graphics Research IGD, Darmstadt 64283, Germany |
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| Abstract: |
| Visual tracking is a challenging problem in computer vision. Recently, correlation filter-based trackers have shown to provide excellent tracking performance. Inspired by a sample consensus approach proposed for foreground detection, which classifies a given pixel as foreground or background based on its similarity to recently observed samples, we present a template consensus tracker based on the kernelized correlation filter (KCF). Instead of keeping only one target appearance model in the KCF, we make a feature pool to keep several target appearance models in our method and predict the new target position by searching for the location of the maximal value of the response maps. Both quantitative and qualitative evaluations are performed on the CVPR2013 tracking benchmark dataset. The results show that our proposed method improves the original KCF tracker by 8.17% in the success plot and 8.11% in the precision plot. |
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