Pedestrian Detection Using Multi-layer LIDAR

To avoid the potential risk of collision between pedestrians and egovehicle, we propose a new pedestrian detection algorithm based on multi-layer LIDAR. First, the points are clustered based on point-distance segmentation, and non-pedestrian clusters are eliminated with physical attributes of pedestrian. Then pedestrian probability is calculated using Bayes rules, and non-parametric kernel density likelihood function estimation model is established to estimate the pose of pedestrian. Moreover, linear Kalman filter is utilized to track the pedestrian which improves the detection accuracy at part occlusion situation. The experiment validates that our algorithm can accurately detect pedestrians in front of ego-vehicle, and the recognition results based on the fusion of detection and tracking processes is better than those from single pedestrian attribute detection.