PointTrackNet: An End-to-End Network for 3-D Object Detection and Tracking from Point Clouds

Sukai Wang, Yuxiang Sun, Chengju Liu, Ming Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

45 Citations (Scopus)


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 point-wise tracking association displacements for each detected object. The network merely takes as input two adjacent point-cloud frames. Experimental results on the KITTI tracking dataset show competitive results over the state-of-the-arts, especially in the irregularly and rapidly changing scenarios.

Original languageEnglish
Article number9000527
Pages (from-to)3206-3212
Number of pages7
JournalIEEE Robotics and Automation Letters
Issue number2
Publication statusPublished - Apr 2020
Externally publishedYes


  • autonomous vehicles
  • end-to-end
  • multiple-object tracking
  • Point cloud

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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