The challenge of developing a robust, real-time driver gaze classification system is that it has to handle difficult edge cases that arise in real-world driving conditions: extreme lighting variations, eyeglass reflections, sunglasses and other occlusions. We propose a single-camera end-toend framework for classifying driver gaze into a discrete set of regions. This framework includes data collection, semi-automated annotation, offline classifier training, and an online real-time image processing pipeline that classifies the gaze region of the driver. We evaluate an implementation of each component on various subsets of a large onroad dataset. The key insight of our work is that robust driver gaze classification in real-world conditions is best approached by leveraging the power of supervised learning to generalize over the edge cases present in large annotated on-road datasets.


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    Title :

    A Framework for Robust Driver Gaze Classification


    Additional title:

    Sae Technical Papers


    Contributors:
    Mehler, Bruce (author) / Reimer, Bryan (author) / Lee, Joonbum (author) / Fridman, Lex (author)

    Conference:

    SAE 2016 World Congress and Exhibition ; 2016



    Publication date :

    2016-04-05




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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