Red Light Running Detection Using AI-Powered Object Tracking on Embedded Systems

This paper presents a novel method for automatic detection of red light running violations based on video analysis and advanced artificial intelligence techniques, specifically designed for embedded platforms. The system integrates a CNN-based traffic light classifier, the YOLOv8 object detector, and the ByteTrack tracking algorithm to identify vehicles and analyze their trajectories relative to traffic signals within user-defined regions of interest. Unlike traditional approaches relying on costly infrastructure or cloud processing, the proposed method runs entirely on-device, enabling real-time inference with reduced latency and increased data security. A prototype was developed on an NVIDIA Jetson Orin Nano and tested using real-world traffic videos under varying lighting and weather conditions. Experimental results demonstrate high detection accuracy and reliable operation, achieving frame rates of up to 29 fps. These findings confirm the method's effectiveness and efficiency, offering a scalable, low-cost solution for intelligent traffic enforcement in resource-constrained environments.