Multi-objective tracking for smart substation onsite surveillance based on YOLO Approach and AKCF
The onsite surveillance plays an important role in the smart substation since the smart substation is unattended. All the sites and operation staff should be supervised throughout the process since a series of risks exist on the working sites. KCF (Kernel Correlation Filter) is an effective method t...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-10-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723008478 |
| _version_ | 1797388842086432768 |
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| author | Dongsheng Cai Zexiang Guan Olusola Bamisile Wenxu Zhang Jian Li Zhenyuan Zhang Jie Wu Zhengwei Chang Qi Huang |
| author_facet | Dongsheng Cai Zexiang Guan Olusola Bamisile Wenxu Zhang Jian Li Zhenyuan Zhang Jie Wu Zhengwei Chang Qi Huang |
| author_sort | Dongsheng Cai |
| collection | DOAJ |
| description | The onsite surveillance plays an important role in the smart substation since the smart substation is unattended. All the sites and operation staff should be supervised throughout the process since a series of risks exist on the working sites. KCF (Kernel Correlation Filter) is an effective method to track a moving object for safety surveillance. However, the occlusion and shape changes worsen the performance of KCF, especially on the occasion of multi-objective detection. This paper proposes a comprehensive method for improving the precision and robustness of detection. Firstly, all the moving objects are detected by the YOLO method. In the tracking part, an AKCF (Augmented Kernel Correlation Filter) is proposed for the heavily occluded object, and the Kalman Filter (KF) serves as a supplementary output. Moreover, in the target association section, based on priority matching and rematching based on motion estimation, a two-stage target association method is proposed. Test outcomes indicate that the proposed algorithm is accurate and robust for tracking workers’ trajectories and conducting surveillance. |
| first_indexed | 2024-03-08T22:46:40Z |
| format | Article |
| id | doaj.art-d3dcd4b75a0740b98c9088906a8cd54d |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-08T22:46:40Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-d3dcd4b75a0740b98c9088906a8cd54d2023-12-17T06:38:50ZengElsevierEnergy Reports2352-48472023-10-01914291438Multi-objective tracking for smart substation onsite surveillance based on YOLO Approach and AKCFDongsheng Cai0Zexiang Guan1Olusola Bamisile2Wenxu Zhang3Jian Li4Zhenyuan Zhang5Jie Wu6Zhengwei Chang7Qi Huang8The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Sichuan P.R., 610059, ChinaThe College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Sichuan P.R., 610059, ChinaThe College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Sichuan P.R., 610059, ChinaSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Sichuan P.R., 611731, ChinaSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Sichuan P.R., 611731, ChinaSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Sichuan P.R., 611731, ChinaState Grid Sichuan Electric Power Company Electric Power Research Institute, Chengdu 610072, Sichuan, ChinaState Grid Sichuan Electric Power Company Electric Power Research Institute, Chengdu 610072, Sichuan, ChinaThe College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Sichuan P.R., 610059, China; School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, Sichuan, China; Corresponding author at: The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Sichuan P.R., 610059, China.The onsite surveillance plays an important role in the smart substation since the smart substation is unattended. All the sites and operation staff should be supervised throughout the process since a series of risks exist on the working sites. KCF (Kernel Correlation Filter) is an effective method to track a moving object for safety surveillance. However, the occlusion and shape changes worsen the performance of KCF, especially on the occasion of multi-objective detection. This paper proposes a comprehensive method for improving the precision and robustness of detection. Firstly, all the moving objects are detected by the YOLO method. In the tracking part, an AKCF (Augmented Kernel Correlation Filter) is proposed for the heavily occluded object, and the Kalman Filter (KF) serves as a supplementary output. Moreover, in the target association section, based on priority matching and rematching based on motion estimation, a two-stage target association method is proposed. Test outcomes indicate that the proposed algorithm is accurate and robust for tracking workers’ trajectories and conducting surveillance.http://www.sciencedirect.com/science/article/pii/S2352484723008478Multi-objective detectionTarget trackingTarget associationDeep learning approachKernel correlation filter |
| spellingShingle | Dongsheng Cai Zexiang Guan Olusola Bamisile Wenxu Zhang Jian Li Zhenyuan Zhang Jie Wu Zhengwei Chang Qi Huang Multi-objective tracking for smart substation onsite surveillance based on YOLO Approach and AKCF Energy Reports Multi-objective detection Target tracking Target association Deep learning approach Kernel correlation filter |
| title | Multi-objective tracking for smart substation onsite surveillance based on YOLO Approach and AKCF |
| title_full | Multi-objective tracking for smart substation onsite surveillance based on YOLO Approach and AKCF |
| title_fullStr | Multi-objective tracking for smart substation onsite surveillance based on YOLO Approach and AKCF |
| title_full_unstemmed | Multi-objective tracking for smart substation onsite surveillance based on YOLO Approach and AKCF |
| title_short | Multi-objective tracking for smart substation onsite surveillance based on YOLO Approach and AKCF |
| title_sort | multi objective tracking for smart substation onsite surveillance based on yolo approach and akcf |
| topic | Multi-objective detection Target tracking Target association Deep learning approach Kernel correlation filter |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723008478 |
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