Pedestrian trajectory is challenging to predict due to its nonlinear nature, and uncertainty about pedestrian movement intentions. At the same time, it is an important information to ensure safety in an environment shared by humans and intelligent vehicles. Many algorithms focus on pedestrian detection to predict its position in a nearby future, combined with external features such as head orientation, visual context from scenarios, social iteration, vehicles information, etc. Nevertheless, the features are usually generated by a human annotator, an expensive task that does not necessarily represent the data quality obtained by a real on-board vision system. Moreover, the use of some variables, such as sequence of images, request more computational processing. We propose a model for predicting pedestrian’s future trajectories, which uses the pedestrian’s key points that represent its pose and position. These key points are used as inputs in a recurrent neural network. We also explore the advantages of using auto extracted features from the latent space of a recurrent autoencoder. Our final approach decreases the mean squared error of the future trajectories of pedestrians and it does not significantly increase computational processing. In addition, it automatically extracts the features without needing a human annotator.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Pedestrian Trajectory Prediction with Pose Representation and Latent Space Variables


    Contributors:


    Publication date :

    2021-10-11


    Size :

    5021278 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Pedestrian trajectory prediction method and device

    ZHENG WEN / LIU CHUANG / XU GUANGYU et al. | European Patent Office | 2023

    Free access

    Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space

    Neumeier, Marion / Betsch, Michael / Tollkuhn, Andreas et al. | IEEE | 2021


    Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information

    Tamaru, Rei / Li, Pei / Ran, Bin | ArXiv | 2024

    Free access

    LG-Traj: LLM Guided Pedestrian Trajectory Prediction

    Chib, Pranav Singh / Singh, Pravendra | ArXiv | 2024

    Free access

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

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