Full understanding human motion is essential for autonomous agents such as self-driving vehicles and social robots for navigating in dense crowded environments. In this paper, we present a trajectory prediction framework which models inter-pedestrian behaviour through graph representations and then apply attention through a Transformer network to better forecast human motion. Previous works have incorporated pedestrian interaction using social and graph pooling mechanisms whereas our work utilizes complete graph structure of pedestrians which helps to obtain robust spatiotemporal representations. We also leverage semantic segmentation architecture to encode scene context. Our experiments highlight the potential of handing pedestrian interaction with graph convolutional networks and Transformer and, on top of that, shows marginal improvement with inclusion of semantic scene features.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    STGT: Forecasting Pedestrian Motion Using Spatio-Temporal Graph Transformer


    Beteiligte:
    Syed, Arsal (Autor:in) / Morris, Brendan (Autor:in)


    Erscheinungsdatum :

    11.07.2021


    Format / Umfang :

    6284369 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    STGT: FORECASTING PEDESTRIAN MOTION USING SPATIO-TEMPORAL GRAPH TRANSFORMER

    Syed, Arsal / Morris, Brendan | British Library Conference Proceedings | 2021



    Scene Spatio-Temporal Graph Convolutional Network for Pedestrian Intention Estimation

    Naik, Abhilash Y. / Bighashdel, Ariyan / Jancura, Pavol et al. | IEEE | 2022



    Spatio-Temporal Graph Attention Convolution Network for Traffic Flow Forecasting

    Liu, Kun / Zhu, Yifan / Wang, Xiao et al. | Transportation Research Record | 2024