Numerous cameras deployed in venues can collect video data, which can be analyzed to help evacuate people in emergency situations. Most surveillance videos can provide data support for pedestrian trajectory prediction. However, data-driven prediction method do not consider the impact of personalization on pedestrian trajectories, which results in ignoring individual personalization. How to accurately analyze the differences among pedestrians and yet accurately predict pedestrian trajectories is a challenging problem. To solve the above problems, we propose an singular value decomposition (SVD) based pedestrian trajectory prediction method, which introduces matrix decomposition to pedestrian trajectory prediction for the first time. Thus, This method analyzes the impact of pedestrian personalization on motion trajectories by mining the interaction patterns between pedestrians and environment. Firstly, we propose an information collection method based on environmental semantics, which can extract scene information from historical video data to construct an environmental information matrix. Secondly, we propose an SVD-based individual environmental feature preference method, which uses singular value decomposition methods to mine the data and analyze personalized pedestrian motion patterns to construct a personalized individual preference matrix. Finally, we built an personalized trajectory prediction method to predict pedestrian movement trajectories. The experimental results show that the method can not only analyze the effect of personalization on pedestrian movement but also accurately predict the pedestrian movement trajectory.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Singular Value Decomposition Based Pedestrian Trajectory Prediction


    Beteiligte:
    Wang, Heng (Autor:in) / Zhang, Guijuan (Autor:in) / Liu, Hong (Autor:in) / Lu, Dianjie (Autor:in)


    Erscheinungsdatum :

    24.05.2023


    Format / Umfang :

    2782704 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Travel Time Prediction Using Singular Value Decomposition

    Nishiuma, N. / Goto, Y. / Kumazawa, H. et al. | British Library Online Contents | 2006


    Pedestrian trajectory prediction method and device

    ZHENG WEN / LIU CHUANG / XU GUANGYU et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review

    Golchoubian, Mahsa / Ghafurian, Moojan / Dautenhahn, Kerstin et al. | IEEE | 2023


    Pedestrian emptying time calculation method based on trajectory prediction

    FU TING / WANG JUNHUA / LI XINYI et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    A Posture Features Based Pedestrian Trajectory Prediction with LSTM

    Kao, I-Hsi / Zhou, Xiao / Chen, I-Ming et al. | IEEE | 2021