Pedestrian Detection and Pose Estimation for Autonomous Driving
Abstract
Autonomous driving systems are right on the corner and one key concern around the development and social acceptance of such systems is safeguarding. In this project, we want to look at the task of pedestrian detection from
LiDAR? point clouds and their 3D pose estimation from the RGB camera input. 3D object detection from sparse point cloud data and 3D multi-person 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 safeguarding metrics.
Requirements
An overall understanding of such concepts is needed:
- 3D computer vision
- Object detection
- Human pose estimation
- Point clouds processing
Hands-on experience with:
- 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
For application please contact via e-mail [m.saleh_at_tum.de] with your latest CV and transcript of records.