Learning to Infer Relations for Future Trajectory Forecast

CVPRW 2019 pp. 2952-2955

doi:10.1109/CVPRW.2019.00356 /cvprw/2019/choi2019cvprw-learning/

Abstract

Inferring relational behavior between road users as well as road users and their surrounding physical space is an important step toward effective modeling and prediction of navigation strategies adopted by participants in road scenes. To this end, we propose a relation-aware framework for future trajectory forecast, which aims to infer relational information from the interactions of road users with each other and with environments. Extensive evaluations on a public benchmark dataset demonstrate the robustness and efficacy of the proposed framework as observed by performances higher than the state-of-the-art methods.

CVPRW Semantic Scholar

Cite

Text

Choi and Dariush. "Learning to Infer Relations for Future Trajectory Forecast." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019. doi:10.1109/CVPRW.2019.00356

Markdown

[Choi and Dariush. "Learning to Infer Relations for Future Trajectory Forecast." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/choi2019cvprw-learning/) doi:10.1109/CVPRW.2019.00356

BibTeX

@inproceedings{choi2019cvprw-learning,
  title     = {{Learning to Infer Relations for Future Trajectory Forecast}},
  author    = {Choi, Chiho and Dariush, Behzad},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2019},
  pages     = {2952-2955},
  doi       = {10.1109/CVPRW.2019.00356},
  url       = {https://mlanthology.org/cvprw/2019/choi2019cvprw-learning/}
}