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.
An overall understanding of such concepts is needed:
Hands-on experience with:
For application please contact via e-mail [m.saleh_at_tum.de] with your latest CV and transcript of records.
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Title: | 3D Pedestrian Detection and Pose Estimation |
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 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 safeguarding KPIs defined. |
Student: | |
Director: | Federico Tombari |
Supervisor: | Mahdi Saleh |
Type: | Hiwi |
Area: | Segmentation, Machine Learning, Computer Vision |
Status: | running |
Start: | |
Finish: | |
Thesis (optional): | |
Picture: |