PF-MOT: Probability Fusion Based 3D Multi-Object Tracking for Autonomous Vehicles
Author:
Affiliation:
1. School of Computer Science, University of Science and Technology of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9811522/9811357/09811653.pdf?arnumber=9811653
Reference29 articles.
1. Multiple Object Tracking with Motion and Appearance Cues
2. How to combine independent data sets for the same quantity
3. Center-based 3D Object Detection and Tracking
4. FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation
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1. Which Framework is Suitable for Online 3D Multi-Object Tracking for Autonomous Driving with Automotive 4D Imaging Radar?;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02
2. RaTrack: Moving Object Detection and Tracking with 4D Radar Point Cloud;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13
3. Semantically Enhanced Multi-Object Detection and Tracking for Autonomous Vehicles;IEEE Transactions on Robotics;2023-12
4. 3D Multi-Object Tracking based on Two-Stage Data Association for Collaborative Perception Scenarios;2023 IEEE Intelligent Vehicles Symposium (IV);2023-06-04
5. GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker Without Bells and Whistles;IEEE Transactions on Intelligent Vehicles;2023-02
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