Driver fatigue detection (DFD) is an effective method to prevent traffic accidents. The existing research on DFD using facial features is an effective and noninvasive fatigue detection method. However, this approach is affected by facial occlusions (glasses, sunglasses, masks, etc.) and the large facial pose deformations in the extraction of effective fatigue features. In this paper, we introduce a novel DFD method using human pose information entropy. The method first estimates human pose from video sequences and then uses them as clues to extract multiple fatigue-related features which can reduce the influence of facial occlusion and head pose deformation. Information entropy and sliding window algorithm are applied to analyse and calculate sufficient consecutive video frames to obtain more robust and accurate fatigue-related values than by using a single frame. These information entropy values are combined resorting to the support vector machine (SVM) to recognize the driver fatigue state. Experimental results show that the method can achieve much higher accuracy and robustness, and the detection speed meets the requirements of real time.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Driver Fatigue Detection Method Based on Human Pose Information Entropy


    Beteiligte:
    Taiguo Li (Autor:in) / Tiance Zhang (Autor:in) / Yingzhi Zhang (Autor:in) / Liben Yang (Autor:in)


    Erscheinungsdatum :

    2022




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Driver Assistance Based on Pose Detection

    HEATH COREY / WITWICKI STEFAN | Europäisches Patentamt | 2025

    Freier Zugriff



    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

    Freier Zugriff