Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms
The major problem in Thailand related to parking is time violation. Vehicles are not allowed to park for more than a specified amount of time. Implementation of closed-circuit television (CCTV) surveillance cameras along with human labor is the present remedy. However, this paper presents an approac...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/13/5843 |
| _version_ | 1797590832412360704 |
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| author | Nabin Sharma Sushish Baral May Phu Paing Rathachai Chawuthai |
| author_facet | Nabin Sharma Sushish Baral May Phu Paing Rathachai Chawuthai |
| author_sort | Nabin Sharma |
| collection | DOAJ |
| description | The major problem in Thailand related to parking is time violation. Vehicles are not allowed to park for more than a specified amount of time. Implementation of closed-circuit television (CCTV) surveillance cameras along with human labor is the present remedy. However, this paper presents an approach that can introduce a low-cost time violation tracking system using CCTV, Deep Learning models, and object tracking algorithms. This approach is fairly new because of its appliance of the SOTA detection technique, object tracking approach, and time boundary implementations. YOLOv8, along with the DeepSORT/OC-SORT algorithm, is utilized for the detection and tracking that allows us to set a timer and track the time violation. Using the same apparatus along with Deep Learning models and algorithms has produced a better system with better performance. The performance of both tracking algorithms was well depicted in the results, obtaining MOTA scores of (1.0, 1.0, 0.96, 0.90) and (1, 0.76, 0.90, 0.83) in four different surveillance data for DeepSORT and OC-SORT, respectively. |
| first_indexed | 2024-03-11T01:29:03Z |
| format | Article |
| id | doaj.art-13ed6638bf18437f802526f3eca54aae |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:29:03Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-13ed6638bf18437f802526f3eca54aae2023-11-18T17:27:35ZengMDPI AGSensors1424-82202023-06-012313584310.3390/s23135843Parking Time Violation Tracking Using YOLOv8 and Tracking AlgorithmsNabin Sharma0Sushish Baral1May Phu Paing2Rathachai Chawuthai3Department of Robotics and AI, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, ThailandDepartment of Robotics and AI, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, ThailandDepartment of Biomedical Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, ThailandDepartment of Computer Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, ThailandThe major problem in Thailand related to parking is time violation. Vehicles are not allowed to park for more than a specified amount of time. Implementation of closed-circuit television (CCTV) surveillance cameras along with human labor is the present remedy. However, this paper presents an approach that can introduce a low-cost time violation tracking system using CCTV, Deep Learning models, and object tracking algorithms. This approach is fairly new because of its appliance of the SOTA detection technique, object tracking approach, and time boundary implementations. YOLOv8, along with the DeepSORT/OC-SORT algorithm, is utilized for the detection and tracking that allows us to set a timer and track the time violation. Using the same apparatus along with Deep Learning models and algorithms has produced a better system with better performance. The performance of both tracking algorithms was well depicted in the results, obtaining MOTA scores of (1.0, 1.0, 0.96, 0.90) and (1, 0.76, 0.90, 0.83) in four different surveillance data for DeepSORT and OC-SORT, respectively.https://www.mdpi.com/1424-8220/23/13/5843DeepSORTOC-SORTobject detectiontracking algorithmvehicle trackingYOLOv8 |
| spellingShingle | Nabin Sharma Sushish Baral May Phu Paing Rathachai Chawuthai Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms Sensors DeepSORT OC-SORT object detection tracking algorithm vehicle tracking YOLOv8 |
| title | Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms |
| title_full | Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms |
| title_fullStr | Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms |
| title_full_unstemmed | Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms |
| title_short | Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms |
| title_sort | parking time violation tracking using yolov8 and tracking algorithms |
| topic | DeepSORT OC-SORT object detection tracking algorithm vehicle tracking YOLOv8 |
| url | https://www.mdpi.com/1424-8220/23/13/5843 |
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