In this work, we present a new approach for start intention detection of cyclists based on 3D human pose estimation to increase their safety in road traffic. Start intention detection is realized using sequences of frame-wise estimated 3D poses. The poses were obtained by image sequences recorded by a stereo camera mounted behind the windshield of a moving vehicle. For training and evaluation, a dataset with 206 starting cyclists was created in real traffic. We demonstrate the advantages of this approach by comparing it to an existing, solely head trajectory based method. In particular, we investigate the performance for different observed time horizons ranging from 0.12 s up to 1.0 s as inputs for the two methods. This is of special importance for the protection of cyclists in road traffic, as they often only become visible to approaching vehicles shortly before dangerous situations occur. With an input length of 1.0 s the solely head trajectory based approach detects starting motions on average 0.834 s after the first motion of the bicycle with an F1-score of 97.5 %. The pose based approach outperforms these results by achieving the same F1-score 0.135 s earlier. The advantages of the pose based method become even more obvious with shorter input lengths. With an input length of 0.12 s, the head based approach achieves an F1-score of 93.5 % after 2.37 s, while the same score is reached after 0.668 s using poses.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Pose Based Start Intention Detection of Cyclists


    Contributors:


    Publication date :

    2019-10-01


    Size :

    555005 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation

    Fang, Zhijie / López, Antonio M. | ArXiv | 2019

    Free access


    Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network

    Zernetsch, Stefan / Kress, Viktor / Sick, Bernhard et al. | IEEE | 2018


    EARLY START INTENTION DETECTION OF CYCLISTS USING MOTION HISTORY IMAGES AND A DEEP RESIDUAL NETWORK

    Zernetsch, Stefan / Kress, Viktor / Sick, Bernhard et al. | British Library Conference Proceedings | 2018


    Cooperative starting intention detection of cyclists based on smart devices and infrastructure

    Bieshaar, Maarten / Zernetsch, Stefan / Depping, Malte et al. | IEEE | 2017