Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms

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dc.contributor.authorSharma, Nabin
dc.contributor.authorBaral, Sushish
dc.contributor.authorPaing, May Phu
dc.contributor.authorChawuthai, Rathachai
dc.date.accessioned2026-08-06T10:41:55Z
dc.date.available2026-08-06T10:41:55Z
dc.date.issued2023-07-01
dc.description.abstractThe 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.
dc.identifier.citationSensors, 23(13), 2023
dc.identifier.doi10.3390/s23135843
dc.identifier.issn14248220
dc.identifier.other2-s2.0-85164847358
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14523
dc.sourceSensors
dc.subjectDeepSORT
dc.subjectobject detection
dc.subjectOC-SORT
dc.subjecttracking algorithm
dc.subjectvehicle tracking
dc.subjectYOLOv8
dc.titleParking Time Violation Tracking Using YOLOv8 and Tracking Algorithms
dc.typeArticle

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