Distracted driving causes many accidents every year, most of which can be avoided with automatic recognition. As a result, vision-based driver action recognition is receiving increasing research attention. In a limited in-vehicle space, actions can be very ambiguous from an individual view. Therefore exploring efficient multi-view action recognition architecture is meaningful. This study aims to detect the distraction of drivers while identifying the cause. A novel driver action recognition architecture named multi-view vision transformer (MVVT) is proposed, which combines classical convolutional neural networks (CNNs) with vision transformer. Self-attention mechanism is utilized to dynamically aggregate temporal information and fuse features of different views jointly. Experiments demonstrate that MVVT can effectively recognize drivers’ behaviors with multi-view input. A promising result of 84.9% accuracy is achieved on a large public driver action dataset.


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    Titel :

    Multi-view Vision Transformer for Driver Action Recognition


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Zhang, Zhenyuan (Herausgeber:in) / Shan, Guangwei (Autor:in) / Ji, Qingge (Autor:in) / Xie, Yuguang (Autor:in)

    Kongress:

    International Conference on Intelligent Transportation Engineering ; 2021 ; Beijing, China October 29, 2021 - October 31, 2021



    Erscheinungsdatum :

    01.06.2022


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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