Correlation Filter Tracking Algorithm Using TSK Fuzzy Logic System
For the problem how to accurately and quickly distinguish the target from the background in the current target tracking field, the core task of most trackers is how to train a discriminant classifier to distinguish between the target and the surrounding environment. At present, the more advanced ker...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2020-02-01
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| Series: | Jisuanji kexue yu tansuo |
| Subjects: | |
| Online Access: | http://fcst.ceaj.org/CN/abstract/abstract2119.shtml |
| _version_ | 1819152445989715968 |
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| author | CHEN Chen, GAO Yanli, DENG Zhaohong, WANG Shitong |
| author_facet | CHEN Chen, GAO Yanli, DENG Zhaohong, WANG Shitong |
| author_sort | CHEN Chen, GAO Yanli, DENG Zhaohong, WANG Shitong |
| collection | DOAJ |
| description | For the problem how to accurately and quickly distinguish the target from the background in the current target tracking field, the core task of most trackers is how to train a discriminant classifier to distinguish between the target and the surrounding environment. At present, the more advanced kernel correlation filter algorithm (KCF) and improved discriminant correlation filter (DCF) can combine the discriminant classifier with the Fourier transform to improve the tracking speed. Some KCF-based optimization algorithms provide solutions to partial tracking problems, such as the KCF algorithm for scale problems and the KCF algorithm for target disappearance. However, existing algorithms still have some room for improvement in improving accuracy. Aiming at this deficiency, a new fuzzy kernel correlation filter (FKCF) is derived using Takagi-Sugeno-Kang fuzzy logic system (TSK-FLS) based on kernel correlation filter. FKCF inherits the characteristics of high speed and small computational complexity of KFC, and further to improve the robustness, replacing the previous simple Gaussian mapping with the fuzzy membership function and introducing the consequent parameters of TSK-FLS in the process of kernel calculation. Thus, FKCF achieves better tracking accuracy than traditional KCF. Extensive experiments are carried out on 50 randomly selected videos on 4 databases such as OTB50. The experimental results show that accuracies of the FKCF on 10 types of common attributes are all improved compared with the traditional KCF. |
| first_indexed | 2024-12-22T14:49:25Z |
| format | Article |
| id | doaj.art-2292e3f444b242039c2968e9dab32a35 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-12-22T14:49:25Z |
| publishDate | 2020-02-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-2292e3f444b242039c2968e9dab32a352022-12-21T18:22:21ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182020-02-0114229430610.3778/j.issn.1673-9418.1810019Correlation Filter Tracking Algorithm Using TSK Fuzzy Logic SystemCHEN Chen, GAO Yanli, DENG Zhaohong, WANG Shitong01. School of Digital Media, Jiangnan University, Wuxi, Jiangsu 214122, China 2. Jiangnan Institute of Computing Technology, Wuxi, Jiangsu 214083, ChinaFor the problem how to accurately and quickly distinguish the target from the background in the current target tracking field, the core task of most trackers is how to train a discriminant classifier to distinguish between the target and the surrounding environment. At present, the more advanced kernel correlation filter algorithm (KCF) and improved discriminant correlation filter (DCF) can combine the discriminant classifier with the Fourier transform to improve the tracking speed. Some KCF-based optimization algorithms provide solutions to partial tracking problems, such as the KCF algorithm for scale problems and the KCF algorithm for target disappearance. However, existing algorithms still have some room for improvement in improving accuracy. Aiming at this deficiency, a new fuzzy kernel correlation filter (FKCF) is derived using Takagi-Sugeno-Kang fuzzy logic system (TSK-FLS) based on kernel correlation filter. FKCF inherits the characteristics of high speed and small computational complexity of KFC, and further to improve the robustness, replacing the previous simple Gaussian mapping with the fuzzy membership function and introducing the consequent parameters of TSK-FLS in the process of kernel calculation. Thus, FKCF achieves better tracking accuracy than traditional KCF. Extensive experiments are carried out on 50 randomly selected videos on 4 databases such as OTB50. The experimental results show that accuracies of the FKCF on 10 types of common attributes are all improved compared with the traditional KCF.http://fcst.ceaj.org/CN/abstract/abstract2119.shtmltrackerdiscriminant classifierfourier transformcorrelation filtertsk fuzzy logic system |
| spellingShingle | CHEN Chen, GAO Yanli, DENG Zhaohong, WANG Shitong Correlation Filter Tracking Algorithm Using TSK Fuzzy Logic System Jisuanji kexue yu tansuo tracker discriminant classifier fourier transform correlation filter tsk fuzzy logic system |
| title | Correlation Filter Tracking Algorithm Using TSK Fuzzy Logic System |
| title_full | Correlation Filter Tracking Algorithm Using TSK Fuzzy Logic System |
| title_fullStr | Correlation Filter Tracking Algorithm Using TSK Fuzzy Logic System |
| title_full_unstemmed | Correlation Filter Tracking Algorithm Using TSK Fuzzy Logic System |
| title_short | Correlation Filter Tracking Algorithm Using TSK Fuzzy Logic System |
| title_sort | correlation filter tracking algorithm using tsk fuzzy logic system |
| topic | tracker discriminant classifier fourier transform correlation filter tsk fuzzy logic system |
| url | http://fcst.ceaj.org/CN/abstract/abstract2119.shtml |
| work_keys_str_mv | AT chenchengaoyanlidengzhaohongwangshitong correlationfiltertrackingalgorithmusingtskfuzzylogicsystem |