Multi-cue pedestrian detection from 3D point cloud data
Hsueh-Ling Tang, Shih-Che Chien, Wen-Huang Cheng, Yung-Yao Chen, Kai‐Lung Hua · 2017
Pedestrian detection is one of the key technologies of driver assistance system. In order to prevent potential collisions, pedestrians should be always accurately identified whether during the day or at night. Since the visual images of the night are not clear, this paper proposes a method for recognizing pedestrians by using a high-definition LIDAR without visual images. In order to handle the long-distance sparse point problem, a novel solution is introduced to improve the performance. The proposed method maps the three-dimensional point cloud to the two-dimensional plane by a distance-aware expansion approach and the corresponding 2D contour and its associated 2D features are then extracted. Based on both 2D and 3D cues, the proposed method obtains significant performance boosts over state-of-the-art approaches by 13% in terms of F1-measure.