SGAP-Gaze: Scene Grid Attention Based Point-of-Gaze Estimation Network for Driver Gaze
Driver gaze estimation is essential for understanding the driver's situational awareness of surrounding traffic. Existing gaze estimation models use driver facial information to predict the Point-of-Gaze (PoG) or the 3D gaze direction vector. We propose a benchmark dataset, Urban Driving-Face Scene Gaze (UD-FSG), comprising synchronized driver-face and traffic-scene images. The scene images provide cues about surrounding traffic, which can help improve the gaze estimation model, along with the face images. We propose SGAP-Gaze, Scene-Grid Attention based Point-of-Gaze estimation network, trained and tested on our UD-FSG dataset, which explicitly incorporates the scene images into the gaze...
| Authors | Pavan Kumar Sharma, Pranamesh Chakraborty |
|---|---|
| Primary category | cs.CV |
| Published | 21 April 2026 |
| Last updated at source | 28 September 2026 |
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