Night Vision Pedestrian Detection Using a Forward-Looking Infrared Camera

This paper describes a night vision pedestrian detection system for autonomous vehicles using an onboard forward-looking infrared (FLIR) camera. Much emphasis has been placed on real-time processing requirements and robustness of the system under different scenarios. At low level, the Haar-like features are used to discriminate infrared pedestrians. AdaBoost learning algorithm is employed to select the most class relevant infrared pedestrian features. For effective sub-windows scanning, a novel keypoint based region of interest (ROI) selection strategy in IR imageries is proposed. The system has been implemented as part of a preliminary real-time driver assistance system of Vision Miracle Intelligence Company, Changsha, China. Experimental results under different urban scenarios prove that the proposed system is robust and efficient.