ML Anthology

Recursive Social Behavior Graph for Trajectory Prediction

Jianhua Sun, Qinhong Jiang, Cewu Lu
CVPR 2020
doi:10.1109/CVPR42600.2020.00074 /cvpr/2020/sun2020cvpr-recursive/

Abstract

Social interaction is an important topic in human trajectory prediction to generate plausible paths. In this paper, we present a novel insight of group-based social interaction model to explore relationships among pedestrians. We recursively extract social representations supervised by group-based annotations and formulate them into a social behavior graph, called Recursive Social Behavior Graph. Our recursive mechanism explores the representation power largely. Graph Convolutional Neural Network then is used to propagate social interaction information in such a graph. With the guidance of Recursive Social Behavior Graph, we surpass state-of-the-art methods on ETH and UCY dataset for 11.1% in ADE and 10.8% in FDE in average, and successfully predict complex social behaviors.

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Cite

Text

Sun et al. "Recursive Social Behavior Graph for Trajectory Prediction." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. doi:10.1109/CVPR42600.2020.00074

Markdown

[Sun et al. "Recursive Social Behavior Graph for Trajectory Prediction." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.](https://mlanthology.org/cvpr/2020/sun2020cvpr-recursive/) doi:10.1109/CVPR42600.2020.00074

BibTeX

@inproceedings{sun2020cvpr-recursive,
  title     = {{Recursive Social Behavior Graph for Trajectory Prediction}},
  author    = {Sun, Jianhua and Jiang, Qinhong and Lu, Cewu},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2020},
  doi       = {10.1109/CVPR42600.2020.00074},
  url       = {https://mlanthology.org/cvpr/2020/sun2020cvpr-recursive/}
}