2020/12/05 by Sandipan Banerjee, Banerjee, Sandipan, Ajjen Joshi +7
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Gaze Tracking and Assistive Technology #Video Surveillance and Tracking Methods #Visual Attention and Saliency Detection
paper · pdf · doi:10.48550/arxiv.2012.02906
openalex publication_date 2020/12/05 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Distracted drivers are dangerous drivers. Equipping advanced driver assistance systems (ADAS) with the ability to detect driver distraction can help prevent accidents and improve driver safety. In order to detect driver distraction, an ADAS must be able to monitor their visual attention. We propose a model that takes as input a patch of the driver's face along with a crop of the eye-region and classifies their glance into 6 coarse regions-of-interest (ROIs) in the vehicle. We demonstrate that an hourglass network, trained with an additional reconstruction loss, allows the model to learn stronger contextual feature representations than a traditional encoder-only classification module. To make the system robust to subject-specific variations in appearance and behavior, we design a personalized hourglass model tuned with an auxiliary input representing the driver's baseline glance behavior. Finally, we present a weakly supervised multi-domain training regimen that enables the hourglass to jointly learn representations from different domains (varying in camera type, angle), utilizing unlabeled samples and thereby reducing annotation cost.