An Improvement of the Camshift Human Tracking Algorithm Based on Deep Learning and the Kalman Filter
Automated human tracking in real time has been applied in many areas such as security, surveillance, traffic control, and robots. In this paper, an improvement of the Camshift human tracking algorithm based on deep learning and the Kalman filter is proposed. To detail an approach by using YOLOv4-tin...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2023/5525744 |
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| _version_ | 1832547866144407552 |
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| author | Van-Truong Nguyen Duc-Tuan Chu Dinh-Hieu Phan Ngoc-Tien Tran |
| author_facet | Van-Truong Nguyen Duc-Tuan Chu Dinh-Hieu Phan Ngoc-Tien Tran |
| author_sort | Van-Truong Nguyen |
| collection | DOAJ |
| description | Automated human tracking in real time has been applied in many areas such as security, surveillance, traffic control, and robots. In this paper, an improvement of the Camshift human tracking algorithm based on deep learning and the Kalman filter is proposed. To detail an approach by using YOLOv4-tiny to detect a human in real time, Camshift is used to track a particular person and the Kalman filter is applied to enhance the performance of this algorithm in case of occlusion, noise, and different light conditions. The experiments show that the combination of YOLOv4-tiny and the improved Camshift algorithm raises the standard of speed as well as robustness. The proposed algorithm is suitable for running in real time and adapts well to the same color and different light conditions. |
| format | Article |
| id | doaj-art-b2b77df3f4894eeca6d9fd7f45aa3df9 |
| institution | Kabale University |
| issn | 1687-9619 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-b2b77df3f4894eeca6d9fd7f45aa3df92025-02-03T06:43:14ZengWileyJournal of Robotics1687-96192023-01-01202310.1155/2023/5525744An Improvement of the Camshift Human Tracking Algorithm Based on Deep Learning and the Kalman FilterVan-Truong Nguyen0Duc-Tuan Chu1Dinh-Hieu Phan2Ngoc-Tien Tran3Department of Mechatronics EngineeringDepartment of Mechatronics EngineeringDepartment of Mechatronics EngineeringDepartment of Mechatronics EngineeringAutomated human tracking in real time has been applied in many areas such as security, surveillance, traffic control, and robots. In this paper, an improvement of the Camshift human tracking algorithm based on deep learning and the Kalman filter is proposed. To detail an approach by using YOLOv4-tiny to detect a human in real time, Camshift is used to track a particular person and the Kalman filter is applied to enhance the performance of this algorithm in case of occlusion, noise, and different light conditions. The experiments show that the combination of YOLOv4-tiny and the improved Camshift algorithm raises the standard of speed as well as robustness. The proposed algorithm is suitable for running in real time and adapts well to the same color and different light conditions.http://dx.doi.org/10.1155/2023/5525744 |
| spellingShingle | Van-Truong Nguyen Duc-Tuan Chu Dinh-Hieu Phan Ngoc-Tien Tran An Improvement of the Camshift Human Tracking Algorithm Based on Deep Learning and the Kalman Filter Journal of Robotics |
| title | An Improvement of the Camshift Human Tracking Algorithm Based on Deep Learning and the Kalman Filter |
| title_full | An Improvement of the Camshift Human Tracking Algorithm Based on Deep Learning and the Kalman Filter |
| title_fullStr | An Improvement of the Camshift Human Tracking Algorithm Based on Deep Learning and the Kalman Filter |
| title_full_unstemmed | An Improvement of the Camshift Human Tracking Algorithm Based on Deep Learning and the Kalman Filter |
| title_short | An Improvement of the Camshift Human Tracking Algorithm Based on Deep Learning and the Kalman Filter |
| title_sort | improvement of the camshift human tracking algorithm based on deep learning and the kalman filter |
| url | http://dx.doi.org/10.1155/2023/5525744 |
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