Vehicle detection in monocular night-time grey-level videos

Road traffic accidents are a problem which is countered by the development of systems that can minimize the number of fatal accidents by providing warnings to the driver, in particularly by vision-based driver assistance systems (VBDAS). Vehicle detection at night-time is very complex compared to day time due to availability of limited features and different illumination conditions. When driving at night-time, vehicles approaching from front are only visible by their headlights. This paper presents a monocular vision system capable of detecting vehicles in front views using a Haar-like feature approach in night-time gray-level video sequences. The approach detects vehicles at night-time using a camera by searching for headlights. Experiments demonstrate the effectiveness of the proposed system.