Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction

Type
Conference Paper

Authors
Mohamed, Abduallah
Qian, Kun
Elhoseiny, Mohamed
Claudel, Christian

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Preprint Posting Date
2020-02-27

Online Publication Date
2020-08-05

Print Publication Date
2020-06

Date
2020-08-05

Abstract
Better machine understanding of pedestrian behaviors enables faster progress in modeling interactions between agents such as autonomous vehicles and humans. Pedestrian trajectories are not only influenced by the pedestrian itself but also by interaction with surrounding objects. Previous methods modeled these interactions by using a variety of aggregation methods that integrate different learned pedestrians states. We propose the Social Spatio-Temporal Graph Convolutional Neural Network (Social-STGCNN), which substitutes the need of aggregation methods by modeling the interactions as a graph. Our results show an improvement over the state of art by 20% on the Final Displacement Error (FDE) and an improvement on the Average Displacement Error (ADE) with 8.5 times less parameters and up to 48 times faster inference speed than previously reported methods. In addition, our model is data efficient, and exceeds previous state of the art on the ADE metric with only 20% of the training data. We propose a kernel function to embed the social interactions between pedestrians within the adjacency matrix. Through qualitative analysis, we show that our model inherited social behaviors that can be expected between pedestrians trajectories. Code is available at https://github.com/abduallahmohamed/Social-STGCNN.

Citation
Mohamed, A., Qian, K., Elhoseiny, M., & Claudel, C. (2020). Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr42600.2020.01443

Publisher
Institute of Electrical and Electronics Engineers (IEEE)

Journal
CVPR 2020

Conference/Event Name
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

DOI
10.1109/CVPR42600.2020.01443

arXiv
2002.11927

Additional Links
https://ieeexplore.ieee.org/document/9156583/
https://ieeexplore.ieee.org/document/9156583/
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9156583

Relations
Is Supplemented By:
  • [Software]
    Title: abduallahmohamed/Social-STGCNN: Code for "Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction" CVPR 2020. Publication Date: 2020-02-27. github: abduallahmohamed/Social-STGCNN Handle: 10754/668129

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