Citation

Ong, Chin Sin and Tee, Connie and Goh, Michael Kah Ong (2024) Traffic Violation Detection Using Computer Vision Techniques. JOIV : International Journal on Informatics Visualization, 8 (3-2). p. 1515. ISSN 2549-9610

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Abstract

The increasing number of road accidents is still a global concern. Traditional approaches to detecting traffic violators on the road, such as radar guns and sensors, are expensive and time-consuming to maintain and install. This often results in inefficient and ineffective detection of traffic violators. This paper proposes a more cost-effective and efficient approach to traffic violation detection utilizing visual data from CCTV footage. Specifically, the method targets two common violations: crossing red lights and overtaking on double lines. In this study, YOLO is integrated for road object detection, providing the detection of vehicles and traffic lights on the road for our system. Then, the Deep SORT tracker tracks detected vehicles, ensuring continuous monitoring over time. An automated lane detection technique is formulated to identify the stopping line/lane for red light violation detection, enabling precise detection of vehicles that cross the stop lane during red light. For overtaking detection, the system detects the double line to serve as the boundary that vehicles should not cross, identifying illegal overtaking. Furthermore, point-line distance calculation is utilized to detect traffic violators by analyzing their tracked trajectories and positions. The proposed solution is evaluated using real-world CCTV footage from online repositories to reflect the real-world scenarios as closely as possible. Experimental results show that the proposed techniques achieve promising detection of real-time traffic violators, which leads to a safer environment for road users.

Item Type:	Article
Uncontrolled Keywords:	Traffic violation detection; computer vision; YOLO; lane detection; state recognition; point-line distance
Subjects:	T Technology > TE Highway engineering. Roads and pavements
Divisions:	Faculty of Information Science and Technology (FIST) Faculty of Law (FOL)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Jan 2025 05:45
Last Modified:	03 Jan 2025 05:45
URII:	http://shdl.mmu.edu.my/id/eprint/13287

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