FORECASTING OF SUDDEN PEDESSTRAIN CROSSING FOR SAFE DRIVING DURING NIGHTS BY HIGH INTENSITY NIGHT VISION CAMERA

Sudden pedestrian crossing (SPC) is the major reason for pedestrian-vehicle crashes. In this paper, we focus on detecting SPCs at night for supporting an advanced driver assistance system using a far-infrared (FIR) camera mounted on the front-roof of a vehicle. Although the thermal temperature of the road is similar or higher than that of the pedestrians during summer nights, many previous researches have focused on pedestrian detection during the winter, spring, or autumn seasons. However, our research concentrates on SPC during the hot summer season because the number of collisions between pedestrians and vehicles in Korea is higher at that time than during the other seasons. For real-time processing, we first decide the optimal levels of the image scaling and search area. We then use our proposed method for detecting virtual reference lines that are associated with road segmentation without using color information, and change these lines according to the turning direction of the vehicle. Pedestrian detection is conducted using a cascade random forest with low-dimensional Haar-like features and oriented center symmetric-local binary patterns. The SPC prediction is assessed based on the likelihood and the spatiotemporal features of the pedestrians, such as their overlapping ratio with virtual reference lines, as well as the direction and magnitude of each pedestrian’s movement. The proposed algorithm was successfully applied to various pedestrian dataset captured by an FIR camera, and the results show that its SPC detection performance is better than those of other methods