SGAP-Gaze fuses driver face, eye, and iris features with transformer attention over scene grids to estimate point-of-gaze, cutting mean pixel error by 23.5% versus prior models on a new urban driving dataset.
A dual-cameras-based driver gaze mapping system with an application on non-driving activities monitoring
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
SGAP-Gaze: Scene Grid Attention Based Point-of-Gaze Estimation Network for Driver Gaze
SGAP-Gaze fuses driver face, eye, and iris features with transformer attention over scene grids to estimate point-of-gaze, cutting mean pixel error by 23.5% versus prior models on a new urban driving dataset.