Pedestrian Detection from a Sparse LIDAR Point-Cloud

—Distribution-Based Voxel Representation for 3DCNN—

Abstract

In recent years, the demand for pedestrian detection using LIDAR is increasing, as it can be used to prevent traffic accidents involving pedestrians. To avoid traffic accidents, detection of distant pedestrians is very important. However, they are scanned sparsely even if a dense-scan LIDAR is used, and this causes the degradation of the detection accuracy. There-fore, pedestrian detection from sparsely-scanned LIDAR point-clouds is expected to be developed. This paper proposes a LIDAR-based pedestrian detection method using 3DCNN. Since it is difficult to train a 3DCNN directly from sparse point-clouds, the proposed method converts them to a voxel representation using the kernel density estimation based on LIDAR characteristics. To evaluate the performance of the proposed method, an experiment using real-world LIDAR data was conducted. The results showed that the proposed method could detect pedestrians more accurately than detectors trained with other conventional features.