Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction
Type
Conference PaperDate
2020-08-05Preprint Posting Date
2020-02-27Online Publication Date
2020-08-05Print Publication Date
2020-06Permanent link to this record
http://hdl.handle.net/10754/661865
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Show full item recordAbstract
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/cvpr42600.2020.01443Journal
CVPR 2020Conference/Event name
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)ISBN
978-1-7281-7169-2arXiv
2002.11927Additional 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
ae974a485f413a2113503eed53cd6c53
10.1109/CVPR42600.2020.01443