Night Time Vehicle Detection and Tracking by Fusing Sensor Cues from Autonomous Vehicles

Abstract

Night time vehicle detection and tracking has been a challenging task in recent years. This paper presents a novel context-aware traffic surveillance system that integrates sensor information from autonomous vehicles to improve performance of night time vehicle detection and tracking. The key elements of the proposed method include a novel vehicle pairing framework that represents vehicles based on the fused sensor contexts and vehicle taillights. These detected vehicles are then tracked in real-time night time traffic videos. Experiments are conducted on real traffic videos and the proposed system attains 0.6319 in multiple object tracking accuracy (MOTA), which represents a 26.1% increase compared with the baseline performance.