Camera-LiDAR Jaywalking Detection in Traffic Surveillance System

Roadside sensors like cameras and LiDAR enhance pedestrian safety by providing comprehensive traffic data. While traditional traffic surveillance systems primarily focus on vehicle-related violations, such as signal violations and speeding, pedestrian jaywalking remains a significant cause of accidents. This paper presents a jaywalking detection method that fuses camera-based image segmentation with LiDAR ground seg- mentation to handle various conditions, including day and night. Our system addresses challenges such as poor lighting and vehicle occlusion by leveraging LiDAR’s robustness in unlearned environments. Road segmentation is enhanced by combining camera outputs with LiDAR ground data, refining road boundary detection for more accurate road area anal- ysis. By integrating road segmentation and object tracking, the system reduces false negatives and improves jaywalking detection. Experimental results from real-road data validate its effectiveness, showing significant potential to enhance traffic surveillance.