2023/11/24 by Dila Dede, Mehmet Ali Sarsıl, Dede, Dila +7 · 3 citations
Computer Science · Engineering · #Artificial intelligence #Autonomous Vehicle Technology and Safety #Computer science #Computer security #Engineering #Identification (biology) #Law enforcement #License #Pedestrian #Schema crosswalk #Transport engineering #Vehicle License Plate Recognition #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2311.16179
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/11/24 · openalex created_date 2023/12/01 · openalex updated_date 2026/07/28
Road traffic accidents pose a significant global public health concern, leading to injuries, fatalities, and vehicle damage. Approximately 1,3 million people lose their lives daily due to traffic accidents [World Health Organization, 2022]. Addressing this issue requires accurate traffic law violation detection systems to ensure adherence to regulations. The integration of Artificial Intelligence algorithms, leveraging machine learning and computer vision, has facilitated the development of precise traffic rule enforcement. This paper illustrates how computer vision and machine learning enable the creation of robust algorithms for detecting various traffic violations. Our model, capable of identifying six common traffic infractions, detects red light violations, illegal use of breakdown lanes, violations of vehicle following distance, breaches of marked crosswalk laws, illegal parking, and parking on marked crosswalks. Utilizing online traffic footage and a self-mounted on-dash camera, we apply the YOLOv5 algorithm's detection module to identify traffic agents such as cars, pedestrians, and traffic signs, and the strongSORT algorithm for continuous interframe tracking. Six discrete algorithms analyze agents' behavior and trajectory to detect violations. Subsequently, an Identification Module extracts vehicle ID information, such as the license plate, to generate violation notices sent to relevant authorities.