  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.
