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

ETH

Pedestrian trajectory forecasting is a fundamental task in multiple utility areas, such as self-driving, autonomous robots, and surveillance systems. The future trajectory forecasting is multi-modal, influenced by physical interaction with scene contexts and intricate social interactions among pedestrians.

Data and Resources

This dataset has no data

Cite this as

Suyi Chen, Hao Xu, Haipeng Li, Kunming Luo, Guanghui Liu, Chi-Wing Fu, Ping Tan, Shuaicheng Liu (2024). Dataset: ETH. https://doi.org/10.57702/vej02xmz

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 3, 2024
Last update December 3, 2024
Defined In https://doi.org/10.48550/arXiv.2010.01114
Citation
  • https://doi.org/10.48550/arXiv.1911.00193
  • https://doi.org/10.48550/arXiv.1803.10892
  • https://doi.org/10.48550/arXiv.2101.10595
  • https://doi.org/10.48550/arXiv.2202.03954
  • https://doi.org/10.48550/arXiv.2004.09760
  • https://doi.org/10.1109/TPAMI.2022.3175371
Author Suyi Chen
More Authors
Hao Xu
Haipeng Li
Kunming Luo
Guanghui Liu
Chi-Wing Fu
Ping Tan
Shuaicheng Liu
Homepage https://github.com/Chen-Suyi/PointRegGPT