LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB
This addresses the need for smooth braking and trust in autonomous vehicles, though it appears incremental as it builds on existing sensor fusion methods.
The paper tackles long-range 3D pedestrian detection for autonomous vehicles by combining RGB and LiDAR data, resulting in a significant improvement in mAP on the KITTI benchmark.
While short range 3D pedestrian detection is sufficient for emergency breaking, long range detections are required for smooth breaking and gaining trust in autonomous vehicles. The current state-of-the-art on the KITTI benchmark performs suboptimal in detecting the position of pedestrians at long range. Thus, we propose an approach specifically targeting long range 3D pedestrian detection (LRPD), leveraging the density of RGB and the precision of LiDAR. Therefore, for proposals, RGB instance segmentation and LiDAR point based proposal generation are combined, followed by a second stage using both sensor modalities symmetrically. This leads to a significant improvement in mAP on long range compared to the current state-of-the art. The evaluation of our LRPD approach was done on the pedestrians from the KITTI benchmark.