Fuzzy Detection Aided Real-Time and Robust Visual Tracking Under Complex Environments
Today, a new generation of artificial intelligence has brought several new research domains such as computer vision (CV). Thus, target tracking, the base of CV, has been a hotspot research domain. Correlation filter (CF)-based algorithm has been the basis of real-time tracking algorithms because of...
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| Vydáno v: | IEEE transactions on fuzzy systems Ročník 29; číslo 1; s. 90 - 102 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
IEEE
01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 1063-6706, 1941-0034 |
| On-line přístup: | Získat plný text |
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| Abstract | Today, a new generation of artificial intelligence has brought several new research domains such as computer vision (CV). Thus, target tracking, the base of CV, has been a hotspot research domain. Correlation filter (CF)-based algorithm has been the basis of real-time tracking algorithms because of the high tracking efficiency. However, CF-based algorithms usually failed to track objects in complex environments. Therefore, this article proposes a fuzzy detection strategy to prejudge the tracking result. If the prejudge process determines that the tracking result is not good enough in the current frame, the stored target template is used for following tracking to avoid the template pollution. During testing on the OTB100 dataset, the experimental results show that the proposed auxiliary detection strategy improves the tracking robustness under complex environment by ensuring the tracking speed. |
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| AbstractList | Today, a new generation of artificial intelligence has brought several new research domains such as computer vision (CV). Thus, target tracking, the base of CV, has been a hotspot research domain. Correlation filter (CF)-based algorithm has been the basis of real-time tracking algorithms because of the high tracking efficiency. However, CF-based algorithms usually failed to track objects in complex environments. Therefore, this article proposes a fuzzy detection strategy to prejudge the tracking result. If the prejudge process determines that the tracking result is not good enough in the current frame, the stored target template is used for following tracking to avoid the template pollution. During testing on the OTB100 dataset, the experimental results show that the proposed auxiliary detection strategy improves the tracking robustness under complex environment by ensuring the tracking speed. |
| Author | Liu, Xinyu Lv, Zhihan Liu, Shuai Wang, Shuai Lin, Chin-Teng |
| Author_xml | – sequence: 1 givenname: Shuai orcidid: 0000-0001-9909-0664 surname: Liu fullname: Liu, Shuai email: cs.liu.shuai@gmail.com organization: Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, and College of Information Science and Engineering, Hunan Normal University, Changsha, China – sequence: 2 givenname: Shuai surname: Wang fullname: Wang, Shuai email: 201920293218@smail.hunnu.edu.cn organization: Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, and College of Information Science and Engineering, Hunan Normal University, Changsha, China – sequence: 3 givenname: Xinyu surname: Liu fullname: Liu, Xinyu email: 201920293208@smail.hunnu.edu.cn organization: Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, and College of Information Science and Engineering, Hunan Normal University, Changsha, China – sequence: 4 givenname: Chin-Teng orcidid: 0000-0001-8371-8197 surname: Lin fullname: Lin, Chin-Teng email: chin-teng.Lin@uts.edu.au organization: Centre for Artificial Intelligence, FEIT, University of Technology Sydney, Sydney, NSW, Australia – sequence: 5 givenname: Zhihan orcidid: 0000-0003-2525-3074 surname: Lv fullname: Lv, Zhihan email: lvzhihan@gmail.com organization: School of Data Science and Software Engineering, Qingdao University, Qingdao, China |
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| SubjectTerms | Algorithms Artificial intelligence Complex environment Computer vision Correlation Domains fuzzy detection Fuzzy logic Fuzzy sets Fuzzy systems Mathematical model Object recognition Optical tracking OTB100 Real time Target tracking Tracking visual tracking Visualization |
| Title | Fuzzy Detection Aided Real-Time and Robust Visual Tracking Under Complex Environments |
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