arXiv Computer Vision
From Laboratory to Road: Evaluating Wearable Gaze Accuracy for Driving
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arXiv Computer Vision
Oct 15
Quantifying the Impact of Motion on 2D Gaze Estimation in Real-World Mobile Interactions
arXiv:2502.10570v3 Announce Type: replace-cross Abstract: Mobile gaze tracking on handheld devices commonly relies on \emph{appearance-based 2D gaze estimation} from the device's \emph{front-facing R...
arXiv Computer Vision
Oct 2
EyeTAG: Eye Trajectory-Aware Gaze Estimation
arXiv:2610.00922v1 Announce Type: new Abstract: Gaze estimation under natural head-eye motion underpins applications from driver monitoring to human-computer interaction. Single-frame methods predict...
arXiv Computer Vision
Sep 1
Drift Calibration in Geometric Eye Tracking Systems
arXiv:2608.29739v1 Announce Type: new Abstract: Geometric eye trackers can provide the spatial accuracy required for gaze-based interaction and multimodal studies, but their measurements remain sensi...
arXiv Computer Vision
Sep 14
Context-Aware Causal Gaze Forecasting for Human-Vehicle Interaction During In-Cabin Tracking Dropouts
The paper introduces the Causal Context-Gated Forecaster (CCGF) for predicting a driver's gaze during dashboard-mounted tracker dropouts. CCGF uses a 60‑frame history of gaze and head pose combined with DINOv3 scene features, and a learned reliability gate adjusts the influence of these inputs as the dropout progresses. Experiments on 2,047 naturalistic driving events show that live scene updates reduce median error by 33% compared to history‑only forecasting, while frozen scene input yields higher error, demonstrating the value of real‑time scene information.
arXiv Computer Vision
Oct 9
Gaze Estimation for Human-Robot Interaction: Analysis Using the NICO Platform
This paper evaluates current gaze estimation methods in a human‑robot interaction setting using the NICO robotic platform. A new annotated dataset was collected, and four state‑of‑the‑art models were tested, showing angular errors comparable to general benchmarks. In the shared workspace, the best median error was 14.57 cm, highlighting practical limitations and prompting recommendations for integrating gaze estimation into HRI systems.
arXiv Machine Learning
Aug 11
Can Webcam Gaze Constrain Mesa-Objectives in Driving Models? An Instrument Precision Analysis
arXiv:2608. 08947v1 Announce Type: cross Abstract: Current hazard detection systems in autonomous driving may develop mesa objectives, learned internal goals that achieve high training performance through spurious correlations rather than genuine hazard recognition.