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Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms.

Nabin Sharma1, Sushish Baral1, May Phu Paing2

  • 1Department of Robotics and AI, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.

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|July 14, 2023
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Summary

This study introduces a low-cost parking time violation tracking system using CCTV and AI. The system effectively monitors vehicles, improving enforcement efficiency and reducing costs associated with manual surveillance.

Keywords:
DeepSORTOC-SORTYOLOv8object detectiontracking algorithmvehicle tracking

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Area of Science:

  • Computer Vision and Artificial Intelligence
  • Traffic Management Systems
  • Surveillance Technology

Background:

  • Parking time violations are a significant issue in Thailand, currently managed by CCTV and human monitoring.
  • Existing methods are resource-intensive and may lack consistent efficiency in detecting violations.
  • There is a need for a cost-effective and automated solution for monitoring parking time limits.

Purpose of the Study:

  • To develop and evaluate a low-cost parking time violation tracking system.
  • To leverage CCTV, Deep Learning, and object tracking for automated violation detection.
  • To assess the performance of state-of-the-art (SOTA) detection and tracking algorithms in this application.

Main Methods:

  • Utilized Closed-Circuit Television (CCTV) for video surveillance.
  • Employed YOLOv8 for object detection and DeepSORT/OC-SORT algorithms for object tracking.
  • Implemented time boundary conditions to identify and log parking time violations.

Main Results:

  • Achieved high performance in tracking algorithms, with Multi-Object Tracking Accuracy (MOTA) scores up to 1.0 for DeepSORT and 0.90 for OC-SORT across various datasets.
  • Demonstrated the system's capability to accurately track vehicles and enforce time limits.
  • The integrated system showed improved performance compared to traditional methods.

Conclusions:

  • The proposed system offers a viable, low-cost solution for automated parking time violation detection.
  • Deep Learning and object tracking algorithms significantly enhance the efficiency and accuracy of surveillance systems.
  • This approach presents a novel and effective method for traffic management and enforcement.