• Login
    View Item 
    •   Home
    • Research
    • Conference Papers
    • View Item
    •   Home
    • Research
    • Conference Papers
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

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

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Preprintfile1.pdf
    Size:
    3.184Mb
    Format:
    PDF
    Description:
    Pre-print
    Download
    Type
    Conference Paper
    Authors
    Mohamed, Abduallah
    Qian, Kun
    Elhoseiny, Mohamed cc
    Claudel, Christian
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-08-05
    Preprint Posting Date
    2020-02-27
    Online Publication Date
    2020-08-05
    Print Publication Date
    2020-06
    Permanent link to this record
    http://hdl.handle.net/10754/661865
    
    Metadata
    Show full item record
    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)
    ISBN
    978-1-7281-7169-2
    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
    ae974a485f413a2113503eed53cd6c53
    10.1109/CVPR42600.2020.01443
    Scopus Count
    Collections
    Conference Papers; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2023  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.