DashGaze: Driver Gaze Through Dashcam
Thrupthi Ann John, Vineeth N. Balasubramanian, C. V. Jawahar
Abstract
Driver gaze monitoring is crucial for road safety, but existing methods rely on expensive, cumbersome technologies like wearable eye trackers or fixed-camera setups. To address this, we propose a low-cost approach using dashcams to capture driver gaze data. We introduce DashGaze, a large-scale dataset for training appearance-based gaze estimation models, featuring over 900,000 frames collected over 10 hours with 28 unique drivers. DashGaze includes synchronized views of the road, driver, and driver’s egocentric perspective, along with the driver’s gaze in both the driver and ego views. We also present DashGazeNet, a baseline model that generalizes well to unseen drivers and diverse conditions, achieving gaze angle errors within 8.5 and gaze location errors within 225 pixels. Our code and data are available at https://github.com/ThrupthiAnn/DashGaze
BibTeX
@inproceedings{iros2025_dashgazedriverga,
title = {DashGaze: Driver Gaze Through Dashcam},
author = {Thrupthi Ann John and Vineeth N. Balasubramanian and C. V. Jawahar},
booktitle = {IROS 2025},
year = {2025}
}