Distraction of the driver by secondary tasks is already dangerous while driving manually but especially in handover situations in an automated mode this can lead to critical situations. Currently, these tasks are not taken into account in most modern cars. We present a system that detects typical distracting secondary tasks in an efficient modular way. We first determine the body pose of the driver and afterwards use recurrent neuronal networks to estimate actions based on sequences of the captured body poses. Our system uses knowledge about the surroundings of the driver that is unique to the car environment. Our evaluation shows that this approach achieves better results than other state of the art systems for action recognition on our dataset.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Body Pose and Context Information for Driver Secondary Task Detection


    Contributors:


    Publication date :

    2018-06-01


    Size :

    1631861 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    BODY POSE AND CONTEXT INFORMATION FOR DRIVER SECONDARY TASK DETECTION

    Martin, Manuel / Popp, Johannes / Anneken, Mathias et al. | British Library Conference Proceedings | 2018


    Body Pose and Context Information for Driver Secondary Task Detection

    Martin, Manuel / Popp, J. / Anneken, M. et al. | Fraunhofer Publica | 2018

    Free access

    Driver Fatigue Detection Method Based on Human Pose Information Entropy

    Taiguo Li / Tiance Zhang / Yingzhi Zhang et al. | DOAJ | 2022

    Free access

    Driver Assistance Based on Pose Detection

    HEATH COREY / WITWICKI STEFAN | European Patent Office | 2025

    Free access

    Viewpoint Invariant 3D Driver Body Pose-Based Activity Recognition

    Martin, Manuel / Lerch, David / Voit, Michael | IEEE | 2023