Ghost Target Detection in 3D Radar Data using Point Cloud based Deep Neural Network

Ghost Target Detection in 3D Radar Data using Point Cloud based Deep Neural Network
Mahdi Chamseddine, Jason Raphael Rambach, Oliver Wasenmüller, Didier Stricker
International Conference on Pattern Recognition. International Conference on Pattern Recognition (ICPR-2020) January 12-15 Milano Italy IEEE 2021 .

Abstract:

Ghost targets are targets that appear at wrong locations in radar data and are caused by the presence of multiple indirect reflections between the target and the sensor. In this work, we introduce the first point based deep learning approach for ghost target detection in 3D radar point clouds. This is done by extending the PointNet network architecture by modifying its input to include radar point features beyond location and introducing skip connections. We compare different input modalities and analyze the effects of the changes we introduced. We also propose an approach for automatic labeling of ghost targets 3D radar data using lidar as reference. The algorithm is trained and tested on real data in various driving scenarios and the tests show promising results in classifying real and ghost radar targets.

Reference:

@inproceedings{chamseddine2021ghost,
  title={Ghost Target Detection in 3D Radar Data using Point Cloud based Deep Neural Network},
  author={Chamseddine, Mahdi and Rambach, Jason and Stricker, Didier and Wasenmuller, Oliver},
  booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
  pages={10398--10403},
  year={2021},
  organization={IEEE}
}