MOT17

Many Multi-Object Tracking (MOT) approaches exploit motion information to associate all the detected objects across frames. However, many methods that rely on filtering-based algorithms, such as the Kalman Filter, often work well in linear motion scenarios but struggle to accurately predict the locations of objects undergoing complex and non-linear movements.

Data and Resources

Cite this as

Siddharth Sagar Nijhawan, Leo Hoshikawa, Atsushi Irie, Masakazu Yoshimura, Junji Otsuka, Takeshi Ohashi (2024). Dataset: MOT17. https://doi.org/10.57702/qhmpqsss

DOI retrieved: December 2, 2024

Additional Info

Field Value
Created December 2, 2024
Last update December 2, 2024
Defined In https://doi.org/10.48550/arXiv.2211.05654
Citation
  • https://doi.org/10.48550/arXiv.2403.08018
  • https://doi.org/10.48550/arXiv.2103.14258
  • https://doi.org/10.48550/arXiv.2405.15755
  • https://doi.org/10.48550/arXiv.2406.13271
Author Siddharth Sagar Nijhawan
More Authors
Leo Hoshikawa
Atsushi Irie
Masakazu Yoshimura
Junji Otsuka
Takeshi Ohashi
Homepage https://ai.google/vision/datasets/MOT17