You're currently viewing an old version of this dataset. To see the current version, click here.

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

This dataset has no data

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

Private DOI This DOI is not yet resolvable.
It is available for use in manuscripts, and will be published when the Dataset is made public.

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