Although vision-based drowsiness detection approaches have achieved great success on empirically organized datasets, it remains far from being satisfactory for deployment in practice. One crucial issue lies in the scarcity and lack of datasets that represent the actual challenges in real-world applications, e.g. tremendous variation and aggregation of visual signs, challenges brought on by different camera positions and camera types. To promote research in this field, we introduce a new large-scale dataset, FatigueView, that is collected by both RGB and infrared (IR) cameras from five different positions. It contains real sleepy driving videos and various visual signs of drowsiness from subtle to obvious, e.g. with 17,403 different yawning sets totaling more than 124 million frames, far more than recent actively used datasets. We also provide hierarchical annotations for each video, ranging from spatial face landmarks and visual signs to temporal drowsiness locations and levels to meet different research requirements. We structurally evaluate representative methods to build viable baselines. With FatigueView, we would like to encourage the community to adapt computer vision models to address practical real-world concerns, particularly the challenges posed by this dataset.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    FatigueView: A Multi-Camera Video Dataset for Vision-Based Drowsiness Detection


    Contributors:
    Yang, Cong (author) / Yang, Zhenyu (author) / Li, Weiyu (author) / See, John (author)


    Publication date :

    2023-01-01


    Size :

    5664471 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Computer Vision Based Driver Assistance Drowsiness Detection

    Emashharawi, Maryam J. S. / Khalifa, Othman O. / Abdul Malik, Noreha et al. | British Library Conference Proceedings | 2022


    Computer Vision Based Driver Assistance Drowsiness Detection

    Emashharawi, Maryam J. S. / Khalifa, Othman O. / Abdul Malik, Noreha et al. | Springer Verlag | 2021


    SUST-DDD: A Real-Drive Dataset for Driver Drowsiness Detection

    Esra Kavalci Yilmaz / M. Ali Akcayol | DOAJ | 2022

    Free access

    CAMERA-BASED EYE BLINK DETECTION ALGORITHM FOR ASSESSING DRIVER DROWSINESS

    Baccour, Mohamed Hedi / Driewer, Frauke / Kasneci, Enkelejda et al. | British Library Conference Proceedings | 2019


    DROWSINESS DETECTION DEVICE AND DROWSINESS DETECTION METHOD

    AGENO FUTOSHI / HIGUCHI DAIKI | European Patent Office | 2022

    Free access