Institution profile

Korea Electronics Technology Institute

Academic institutionasia · kr
Official website
Research library31linked papers
Opportunities0open roles
Selected work

Representative Papers

Real-Time Sleepiness Detection for Driver State Monitoring System

Dec 19, 2015

To address the risk of traffic accidents caused by driver fatigue, this paper proposes a real-time drowsiness detection method. The approach integrates online dynamic template matching with Kalman filtering to achieve robust eye tracking under varying illumination and head pose disturbances. Eye state (open/closed) classification is performed at millisecond-level latency using Histogram of Oriented Gradients (HOG) features and a Support Vector Machine (SVM) classifier; an alarm is triggered when consecutive eye closure exceeds a predefined threshold. The key innovation lies in a novel tracking framework that synergistically combines adaptive template updating with motion prediction, significantly enhancing stability in complex in-vehicle environments. Experimental results demonstrate an average system response latency of less than 30 ms and an eye-closure classification accuracy of 94.2%, satisfying both real-time processing and reliability requirements for embedded automotive platforms.

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Gaze-Anchored Social Net: Decoding Implicit Relations via Joint Modeling

Jul 24, 2026

This work addresses the challenge of modeling gaze behavior driven by implicit social intent, which existing methods struggle to capture due to their tendency to process individuals in isolation or assign social relationships post hoc. To overcome this limitation, we propose ANCHOR, a novel framework that treats social intent as a latent structural driver of gaze behavior and jointly models the distributions of visual attention and implicit social relations in a target-centric manner. Our approach integrates a relational attention mechanism, feature-level modulation, and a single-backbone multi-task architecture, complemented by a flat minima–guided co-optimization strategy to mitigate gradient conflicts between gaze localization and social reasoning. Evaluated on an extended benchmark featuring dense multi-person annotations and a new social influence ranking metric, ANCHOR achieves state-of-the-art performance and provides the first quantitative evidence that static gaze patterns can robustly disentangle and learn implicit social hierarchies.

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Recent publications

Latest Papers

Gaze-Anchored Social Net: Decoding Implicit Relations via Joint Modeling

Jul 24, 2026

This work addresses the challenge of modeling gaze behavior driven by implicit social intent, which existing methods struggle to capture due to their tendency to process individuals in isolation or assign social relationships post hoc. To overcome this limitation, we propose ANCHOR, a novel framework that treats social intent as a latent structural driver of gaze behavior and jointly models the distributions of visual attention and implicit social relations in a target-centric manner. Our approach integrates a relational attention mechanism, feature-level modulation, and a single-backbone multi-task architecture, complemented by a flat minima–guided co-optimization strategy to mitigate gradient conflicts between gaze localization and social reasoning. Evaluated on an extended benchmark featuring dense multi-person annotations and a new social influence ranking metric, ANCHOR achieves state-of-the-art performance and provides the first quantitative evidence that static gaze patterns can robustly disentangle and learn implicit social hierarchies.

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URHead: A Unified UV-Space Representation for Joint Mesh-3DGS Optimization in Head Avatars

Jul 08, 2026

Existing avatar modeling approaches struggle to simultaneously achieve geometric controllability and photorealistic fidelity: mesh-based methods offer structural precision but lack fine details, while 3D Gaussian splatting (3DGS) methods produce high realism at the cost of temporal or structural consistency. To address this trade-off, this work proposes a unified optimization framework that integrates mesh and 3DGS representations in UV space. By leveraging shared parameterization and adaptive Gaussian sampling, the method decouples and jointly optimizes the two representations, enabling them to complement each other effectively. This approach presents the first deep integration of meshes and 3DGS in the UV domain, preserving intricate appearance details while ensuring animation consistency. Extensive evaluations demonstrate superior performance over state-of-the-art methods in both reconstruction quality and drivability.

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