• DOI:10.1109/LRA.2020.2974392
Corpus ID: 211506236

PointTrackNet: An End-to-End Network For 3-D Object Detection and Tracking From Point Clouds

@article{Wang2020PointTrackNetAE,
  title={PointTrackNet: An End-to-End Network For 3-D Object Detection and Tracking From Point Clouds},
  author={Sukai Wang and Yuxiang Sun and Chengju Liu and M. Liu},
  journal={IEEE Robotics and Automation Letters},
  year={2020},
  volume={5},
  pages={3206-3212}
}

• Sukai Wang, Yuxiang Sun, +1 author M. Liu
• Published 2020
• Computer Science
• IEEE Robotics and Automation Letters

Recent machine learning-based multi-object tracking (MOT) frameworks are becoming popular for 3-D point clouds. Most traditional tracking approaches use filters (e.g., Kalman filter or particle filter) to predict object locations in a time sequence, however, they are vulnerable to extreme motion conditions, such as sudden braking and turning. In this letter, we propose PointTrackNet, an end-to-end 3-D object detection and tracking network, to generate foreground masks, 3-D bounding boxes, and… Expand

View on IEEE

arxiv.org

Save to Library

Create Alert

Cite

Launch Research Feed