Self Supervised Learning for Multiple Object Tracking in 3D Point Clouds
Author:
Affiliation:
1. Center for Research in Computer Vision, University of Central Florida,USA
2. University of Western Australia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9981026/9981028/09981793.pdf?arnumber=9981793
Reference54 articles.
1. RetinaTrack: Online Single Stage Joint Detection and Tracking
2. Deep Network Flow for Multi-object Tracking
3. nuScenes: A Multimodal Dataset for Autonomous Driving
4. Tracking Without Bells and Whistles
5. Performance Measures and a Data Set for Multi-target, Multi-camera Tracking
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2. UnLoc: A Universal Localization Method for Autonomous Vehicles using LiDAR, Radar and/or Camera Input;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01
3. TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial Videos;2023 IEEE International Conference on Robotics and Automation (ICRA);2023-05-29
4. 3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds;2023 IEEE International Conference on Robotics and Automation (ICRA);2023-05-29
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