Multi-object Tracking Fusion
Multi-Object Tracking Fusion is a methodological framework within computer vision that integrates heterogeneous sensory inputs or tracking outputs across spatial and temporal domains to jointly estimate the trajectories of multiple objects with improved accuracy and robustness over standalone tracking systems.
82
Publications
4.3K
Citations
320
Authors
112
Institutions
Publications per year
2017–2026
61
Parents
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Robust Multi-Modality Multi-Object Tracking
Wenwei Zhang, Hui Zhou, Shuyang Sun et al. · 2019 · 236 citations
Gaoang Wang, Yizhou Wang, Haotian Zhang et al. · 2019 · 167 citations
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RWTH Aachen University
Aachen, Germany
4 h-index
18 publications
1.1K citations

University of Electronic Science and Technology of China
Chengdu, China
3 h-index
9 publications
607 citations
Technical University of Munich
Munich, Germany
3 h-index
6 publications
364 citations

Harbin Institute of Technology
Harbin, China
3 h-index
9 publications
429 citations
Tsinghua University
Beijing, China
3 h-index
13 publications
948 citations
University of Washington
Seattle, United States
3 h-index
12 publications
1K citations

South China University of Technology
Guangzhou, China
2 h-index
7 publications
163 citations

National University of Defense Technology
Changsha, China
2 h-index
7 publications
143 citations
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Repository · 9 publications · 197 citations
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4 publications · 191 citations
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4 publications · 94 citations
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2 publications · 76 citations
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2 publications · 38 citations
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