DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion With Deep Association-Reference-Cited by-同舟云学术

DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion With Deep Association

Published:2022-07 Issue:3 Volume:7 Page:8260-8267
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Wang Xiyang¹^ORCID,Fu Chunyun²^ORCID,Li Zhankun¹^ORCID,Lai Ying³^ORCID,He Jiawei¹^ORCID

Affiliation:

1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China

2. State Key Laboratory of Mechanical Transmissions and the College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China

3. School of Mechanical Engineering, Guangdong Songshan Polytechnic, Guangdong, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

http://xplorestaging.ieee.org/ielx7/7083369/9750005/09810346.pdf?arnumber=9810346

Reference44 articles.

1. HOTA: A Higher Order Metric for Evaluating Multi-object Tracking

2. Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics

3. DEFT: Detection embeddings for tracking;chaabane,2021

4. Quasi-Dense Similarity Learning for Multiple Object Tracking

5. Track to Reconstruct and Reconstruct to Track

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