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

Authors

320

  1. BL
    Bastian Leibe

    RWTH Aachen University, Germany

    4 publications · 566 citations

    4

    h-index

  2. BR
    Bodo Rosenhahn

    Leibniz University Hannover, Germany

    3 publications · 201 citations

    3

    h-index

  3. LL
    Laura Leal-Taixé

    Technical University of Munich, Germany

    3 publications · 201 citations

    3

    h-index

  4. KK
    Kris Kitani

    Carnegie Mellon University, United States

    3 publications · 338 citations

    3

    h-index

  5. RH
    Roberto Henschel

    Leibniz University Hannover, Germany

    3 publications · 201 citations

    3

    h-index

1–5 of 320

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 197 citations

    8

    h-index

  2. IEEE Access

    4 publications · 191 citations

    4

    h-index

  3. IEEE Transactions on Multimedia

    4 publications · 94 citations

    4

    h-index

  4. IEEE Sensors Journal

    2 publications · 76 citations

    2

    h-index

  5. Remote Sensing

    2 publications · 38 citations

    2

    h-index

1–5