High-Level Traffic-Violation Detection for Embedded Traffic Analysis

This paper presents the design of a robust and real-time traffic-violation detection system for cameras on intersections. We use background segmentation and a novel road-model to obtain the candidate traffic participants. A region-based tracking system, equipped with static occlusion-reasoning, tracks the positions of the objects in the scene. A computationally efficient camera model is defined which only requires three input parameters and enables the extraction of key object parameters like vehicle type and speed. Experiments have shown that an impressive average processing rate of 63-150 Hz is achieved, with high average correct road detection and object-type classification rates of 93-94% and event detection accuracy of 85%.