Real time monitoring of driver’s attention and fatigue typically is approached in controlled conditions, which cannot be valid for real-world applications. This work focuses on the use of deep learning to classify the inattentive states providing a classification model independent from a specific training for each single user. The database for the experiment is collected through a camera mounted on the car dash, recording live video with adverse light conditions. The proposed system proved to have high accuracy at a low computational cost with a fast processing time, viable for real time car applications, and tested in a ground-truth environment, far from the favourable controlled conditions of a simulator. Moreover, this approach offers an improvement in the classification stage, being error-free when pre-training the system in the detection of fatigue and inattention of a specific user and, with minimal error on a random user dataset.


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    Title :

    Detection of driver’s inattention: a real-time deep learning approach


    Contributors:
    Tryhub, S. (author) / Masala, G. L. (author)


    Publication date :

    2019-10-01


    Size :

    449594 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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