Nighttime Vehicle Detection and Tracking with Occlusion Handling by Pairing Headlights and Taillights

Abstract

In recent years, vision-based vehicle detection has received considerable attention in the literature. Depending on the ambient illuminance, vehicle detection methods are classified as daytime and nighttime detection methods. In this paper, we propose a nighttime vehicle detection and tracking method with occlusion handling based on vehicle lights. First, bright blobs that may be vehicle lights are segmented in the captured image. Then, a machine learning-based method is proposed to classify whether the bright blobs are headlights, taillights, or other illuminant objects. Subsequently, the detected vehicle lights are tracked to further facilitate the determination of the vehicle position. As one vehicle is indicated by one or two light pairs, a light pairing process using spatiotemporal features is applied to pair vehicle lights. Finally, vehicle tracking with occlusion handling is applied to refine incorrect detections under various traffic situations. Experiments on two-lane and four-lane urban roads are conducted, and a quantitative evaluation of the results shows the effectiveness of the proposed method.