The rise of intelligent vehicle systems will lead to more human-machine interactions and so there is a need to create a bridge between the system and the actions and behaviours of the people inside the vehicle. In this paper, we propose a dual camera setup to monitor the actions and behaviour of vehicle passengers and a deep learning architecture which can utilise video data to classify a range of actions. The method incorporates two different views as input to a 3D convolutional network and uses transfer learning from other action recognition data. The performance of this method is evaluated using an in-vehicle dataset, which contains video recordings of people performing a range of common in-vehicle actions. We show that the combination of transfer learning and using dual viewpoints in a 3D action recognition network offers an increase in classification accuracy of action classes with distinct poses, e.g. mobile phone use and sleeping, whilst it does not apply as well for classifying those actions with small movements, such as talking and eating.


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    Titel :

    Dual Viewpoint Passenger State Classification Using 3D CNNs


    Beteiligte:
    Tu, Ian (Autor:in) / Bhalerao, Abhir (Autor:in) / Griffiths, Nathan (Autor:in) / Delgado, Mauricio Munoz (Autor:in) / Thomason, Alasdair (Autor:in) / Popham, Thomas (Autor:in) / Mouzakitis, Alex (Autor:in)


    Erscheinungsdatum :

    01.06.2018


    Format / Umfang :

    3046866 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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