Pedestrian Planar LiDAR Pose (PPLP) Network for Oriented Pedestrian Detection Based on Planar LiDAR and Monocular Images

Abstract

Pedestrian detection is an important task for human-robot interaction and autonomous driving applications. Most previous pedestrian detection methods rely on data collected from three-dimensional (3D) Light Detection and Ranging (LiDAR) sensors in addition to camera imagery, which can be expensive to deploy. In this letter, we propose a novel Pedestrian Planar LiDAR Pose Network (PPLP Net) based on two-dimensional (2D) LiDAR data and monocular camera imagery, which offers a far more affordable solution to the oriented pedestrian detection problem. The proposed PPLP Net consists of three sub-networks: an orientation detection network (OrientNet), a Region Proposal Network (RPN), and a PredictorNet. The OrientNet leverages state-of-the-art neural-network-based 2D pedestrian detection algorithms, including Mask R-CNN and ResNet, to detect the Bird's Eye View (BEV) orientation of each pedestrian. The RPN transfers 2D LiDAR point clouds into occupancy grid map and uses a frustum-based matching strategy for estimating non-oriented 3D pedestrian bounding boxes. Outputs from both OrientNet and RPN are passed through the PredictorNet for a final regression. The overall outputs of our proposed network are 3D bounding box locations and orientation values for all pedestrians in the scene. We present oriented pedestrian detection results on two datasets, the CMU Panoptic Dataset and a newly collected FCAV M-Air Pedestrian (FMP) Dataset, and show that our proposed PPLP network based on 2D LiDAR and monocular camera achieves similar or better performance to previous state-of-the-art 3D-LiDAR-based pedestrian detection methods in both indoor and outdoor environments.