Pedestrian Detection and Pose Estimation for Autonomous Driving
Abstract
Autonomous driving systems are right on the corner and one key concern around development and social acceptance of such systems is safe-guarding. In this project, we want to look at the task of Pedestrian detection from
LiDAR? point clouds and their pose estimation from the RGB camera input. 3D object detection from sparse point cloud data and multiple pedestrian 3D pose estimation are two challenging tasks and therefore active research fields in both academia and industry. In this project, we want to integrate the state of the art deep learning methods, train models on synthetic renderings and improve their performance based on safe-guarding KPIs designed.
Requirements
An overall understanding of such concepts is needed:
- 3D computer vision
- Object detection
- Human pose estimation
- Point clouds processing
Hands-on experience on:
- Deep Learning
- PyTorch
- Dockers
- Git
Literature
- Qi, Charles R., et al. "Pointnet: Deep learning on point sets for 3d classification and segmentation." CVPR. 2017.
- Shi, Shaoshuai, Xiaogang Wang, and Hongsheng Li. "Pointrcnn: 3d object proposal generation and detection from point cloud." CVPR. 2019.
- Zhou, Yin, and Oncel Tuzel. "Voxelnet: End-to-end learning for point cloud based 3d object detection." CVPR. 2018.
- Pavlakos, Georgios, et al. "Coarse-to-fine volumetric prediction for single-image 3D human pose." CVPR 2017.
- Mehta, Dushyant, et al. "Xnect: Real-time multi-person 3d human pose estimation with a single rgb camera." arXiv:1907.00837 2019.
Application
Applicants, please contact me via e-mail with you latest transcripts of records and CV:
Mahdi Saleh