Multi Objects Tracking in Nighttime Traffic Scenes

Abstract

As road networks become more congested, traffic surveillance using computer vision techniques is increasingly important. Traffic surveillance can help in improving road network efficiency, re-routing traffic when accidents occur and minimizing delays. Although, there are many algorithms developed to detect and track moving vehicles in daytime, only a handful of techniques have been proposed for nighttime traffic scenes. In the night environment, the moving vehicles are commonly identified by detecting and locating vehicle headlights and taillights. This paper proposes an effective method for detecting and tracking moving vehicles in nighttime. The proposed method identifies vehicles by detecting and locating vehicle lights using automatic thresholding and connected components extraction. Detected lamps are then paired using rule based component analysis approach and tracked using Kalman Filter (KF). The automatic thresholding approach provides a robust and adaptable detection process that operates well under various nighttime illumination conditions. Furthermore, most nighttime tracking algorithms detects vehicles by locating either headlights or rear lights. However, the proposed method has the ability to track vehicles through detecting vehicle headlights and/or rear lights. Several experiments are presented that demonstrate the feasibility and the effectiveness of the proposed method to detect and track vehicles in various nighttime environments.