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SGAP-Gaze network uses scene attention for improved driver gaze estimation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SGAP-Gaze, a novel network for estimating driver gaze that integrates both facial and surrounding scene information. This approach uses a Transformer-based attention mechanism to fuse features from driver faces and traffic scenes, creating a more comprehensive gaze intent vector. The model achieved a significant reduction in mean pixel error compared to existing methods on the Urban Driving-Face Scene Gaze (UD-FSG) dataset, demonstrating improved accuracy in real-world driving scenarios. AI

IMPACT Improves driver gaze estimation accuracy by integrating scene context, potentially enhancing driver monitoring systems.

RANK_REASON This is a research paper describing a new model and dataset for driver gaze estimation.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SGAP-Gaze network uses scene attention for improved driver gaze estimation

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This is a research paper describing a new model and dataset for driver gaze estimation.
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    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…

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