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

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

DOI retrieved: December 3, 2024

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