Building a Production-Ready Traffic Violation Detection System with Computer Vision

The problem of traffic monitoring and violation detection is a complex one that involves variations in lighting, camera angles, occlusions, vehicle density, and inconsistent road markings, making rule-based approaches unreliable at scale. The goal of the project was to design and implement an end-to-end traffic violation detection system using modern computer vision techniques. The system was required to detect vehicles, track them across frames, understand movement patterns, and identify violations in a way that could realistically work in production. The core problem was to automatically detect and analyze vehicle behavior from video streams to identify traffic violations such as illegal turns, lane violations, or restricted-area movement. The system architecture consisted of five stages: video ingestion, vehicle detection, multi-object tracking, violation logic and analytics, and visualization and reporting. For vehicle detection, YOLO-based models were used due to their balance of speed and accuracy in real-time scenarios, and SAHI was used to improve detection accuracy on high-resolution frames. The system used DeepSORT for multi-object tracking, which combines Kalman filtering for motion prediction and appearance embeddings for identity consistency. The violation detection logic was based on region-based logic, directional flow analysis, and temporal thresholds to reduce false positives. The system was designed to be modular, with components that could be improved independently, and the full implementation is available on GitHub.