Distraction Detection using Mage-Net

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Pradipta Biswas
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Distraction detection systems in automotive has great importance due to the prime safety of passengers. Earlier approaches confined to use indirect methods of driving performance metrics to detect visual distraction. Recent methods attempted to develop dedicated classification models for gaze zone estimation whose cross-domain performance was not investigated. We adopt a more generic appearance-based gaze estimation approach where no assumption on setting or participant was made. We proposed MAGE-Net with less number of parameters while achieving on par performance with state of the art techniques on MPIIGaze dataset. We utilized the proposed MAGE-Net and performed a cross-domain evaluation in automotive setting with 10 participants. We observed that the gaze region error using MAGE-Net for interior regions of car is 15.61 cm and 15.13 cm in x and y directions respectively. We utilized these results and demonstrated the capability of proposed system to detect visual distraction using a driving simulator.

Citation: LRD Murthy, A. Mukhopadhyay and P. Biswas, Distraction Detection in Automotive Environment using Appearance-based Gaze Estimation, ACM International Conference on Intelligent User Interfaces (IUI 2022)

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