Pedestrian protection systems are being included by many automobile manufacturers in their commercial vehicles. However, improving the accuracy of these systems is imperative since the difference between an effective and a non-effective intervention can depend only on a few centimeters or on a fraction of a second. In this paper, we describe a method to carry out the prediction of pedestrian locations and pose and to classify intentions up to 1 s ahead in time applying Balanced Gaussian Process Dynamical Models (B-GPDM) and naïve-Bayes classifiers. These classifiers are combined in order to increase the action classification precision. The system provides accurate path predictions with mean errors of 24.4 cm, for walking trajectories, 26.67 cm, for stopping trajectories and 37.36 cm for starting trajectories, at a time horizon of 1 second.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Pedestrian Intention and Pose Prediction through Dynamical Models and Behaviour Classification


    Contributors:
    Quintero, R. (author) / Parra, I. (author) / Llorca, D. F. (author) / Sotelo, M. A. (author)


    Publication date :

    2015-09-01


    Size :

    756292 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Pedestrian Path, Pose and Intention Prediction through Gaussian Process Dynamical Models and Pedestrian Activity Recognition

    Quintero, Raul / Parra, Ignacio / Llorca, David Fernandez et al. | ArXiv | 2020

    Free access

    Pedestrian Path, Pose, and Intention Prediction Through Gaussian Process Dynamical Models and Pedestrian Activity Recognition

    Quintero Minguez, Raul / Parra Alonso, Ignacio / Fernandez-Llorca, David et al. | IEEE | 2019


    Pedestrian Crossing Intention Prediction at Red-Light Using Pose Estimation

    Zhang, Shile / Abdel-Aty, Mohamed / Wu, Yina et al. | IEEE | 2022


    Learning to Forecast Pedestrian Intention from Pose Dynamics

    Ghori, Omair / Mackowiak, Radek / Bautista, Miguel et al. | IEEE | 2018


    LEARNING TO FORECAST PEDESTRIAN INTENTION FROM POSE DYNAMICS

    Ghori, Omair / Mackowiak, Radek / Bautista, Miguel et al. | British Library Conference Proceedings | 2018