Pedestrians’ red-light crossing can present a threat to traffic safety. Among all the existing work related to pedestrian’s red-light crossing, there are few studies using trajectory data in time sequence. This paper uses pose estimation (keypoint detection) to generate pedestrians’ variables from CCTV videos. Four machine learning models are used to predict pedestrians’ crossing intention at intersections’ red-light. The best model achieves an accuracy of 0.920 and AUC value of 0.849, with data from three intersections. Different prediction horizons (up to 4 sec) are used. With longer prediction horizons, the sample size gets smaller, which partially leads to worse model performance. However, the performance with prediction horizon up to 2 sec is still good (AUC value as 0.841). It is found that keypoint variables such as the angles between ankle and knee (left side) and elbow and shoulder (right side) are important. This model can be further implemented in the Infrastructure-to-Vehicle (I2V) applications and thus prevent accidents due to pedestrians’ red-light crossing by issuing warnings to drivers.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Pedestrian Crossing Intention Prediction at Red-Light Using Pose Estimation


    Beteiligte:
    Zhang, Shile (Autor:in) / Abdel-Aty, Mohamed (Autor:in) / Wu, Yina (Autor:in) / Zheng, Ou (Autor:in)


    Erscheinungsdatum :

    2022-03-01


    Format / Umfang :

    2533927 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Early Warning Pedestrian Crossing Intention From Its Head Gesture using Head Pose Estimation

    Perdana, Muhammad Ilham / Anggraeni, Wiwik / Sidharta, Hanugra Aulia et al. | IEEE | 2021


    Pedestrian Graph: Pedestrian Crossing Prediction Based on 2D Pose Estimation and Graph Convolutional Networks

    Cadena, Pablo Rodrigo Gantier / Yang, Ming / Qian, Yeqiang et al. | IEEE | 2019


    PIT: Progressive Interaction Transformer for Pedestrian Crossing Intention Prediction

    Zhou, Yuchen / Tan, Guang / Zhong, Rui et al. | IEEE | 2023


    Traffic Light and Uncertainty Aware Pedestrian Crossing Intention Prediction for Automated Vehicles

    Upreti, Minali / Ramesh, Jayanth / Kumar, Chandan et al. | IEEE | 2023


    Probability estimation for pedestrian crossing intention at signalized crosswalks

    Hashimoto, Yoriyoshi / Gu, Yanlei / Hsu, Li-Ta et al. | IEEE | 2015