LiDAR-Assisted 3D Human Detection for Video Surveillance
Miquel Romero Blanch, Zenjie Li, Sérgio Escalera, Kamal Nasrollahi · 2024
This work explores 3D object detection using LiDAR technology, specifically focusing on pedestrian detection for video surveillance. While LiDAR is well-established in au-tonomous driving, its application in video surveillance is underexplored. We adapt state-of-the-art autonomous driving models for video surveillance, with CenterPoint being the top performer. Optimizing hype rparamete rs, such as voxel size and sweep merging, enhances pedestrian de-tection. Incorporating larger range data aids in gener-alization for video surveillance scenarios. This research demonstrates the feasibility of pedestrian detection in video surveillance and highlights open challenges related to do-main adaptation and the high cost of high-resolution LiDAR sensors. Code: https://github.com/OMiquel/OpenPCDet-video-surveiIIanceo