EyeCue：通过眼动增强的视点视频理解进行驾驶员认知分心检测
EyeCue: Driver Cognitive Distraction Detection via Gaze-Empowered Egocentric Video Understanding
AI总结：
本文提出EyeCue框架，通过眼动与视觉上下文的交互建模，实现驾驶员认知分心检测，利用CogDrive数据集验证其在多种驾驶场景下的高准确率和泛化能力。
摘要：
驾驶员认知分心是道路碰撞的主要原因，且难以检测。与手动或视觉分心不同，认知分心是通过与驾驶无关的思维转移，即使驾驶员看起来视觉专注且无明显身体动作。本文提出EyeCue，一种基于眼动增强的视点视频理解框架，用于检测驾驶员认知分心。关键见解是认知分心表现为眼动与视觉上下文的交互。为捕捉这种交互，EyeCue将眼动与视点视频结合，以实现对驾驶员注意力随时间变化的上下文感知建模。此外，为解决现有数据集规模和多样性有限的问题，我们引入CogDrive，一个综合多场景数据集，通过添加认知分心注释来增强四个现有驾驶数据集。通过在CogDrive上的广泛评估，我们证明EyeCue在准确率上达到74.38%，优于11个基线模型，其中7个来自6个模型家族。值得注意的是，EyeCue在各种驾驶场景（不同道路类型、时间、天气条件）中均能实现超过70%的准确率，具有强大的泛化能力。这些结果突显了建模眼动-上下文交互的重要性以及跨模态交互建模在多模态认知分心检测中的有效性。我们的代码和CogDrive数据集资源可在https://github.com/langzhang2000/EyeCue上获得。
英文摘要：
Driver cognitive distraction is a major cause of road collisions and remains difficult to detect. Unlike manual or visual distraction, cognitive distraction is diverted by thoughts unrelated to driving, even when the driver appears visually attentive and exhibits no explicit physical movements. In this work, we propose EyeCue, a gaze-empowered egocentric video understanding framework, to detect driver cognitive distraction. A key insight is that cognitive distraction manifests in the interaction between eye gaze and visual context. To capture this interaction, EyeCue integrates eye gaze with egocentric video to enable context-aware modeling of the driver's attention over time. Furthermore, to tackle the limited scale and diversity of existing datasets, we introduce CogDrive, a comprehensive multi-scenario dataset that augments four existing driving datasets with cognitive distraction annotations. Through extensive evaluations on CogDrive, we show that EyeCue achieves the highest accuracy of 74.38%, outperforming 11 baselines from 6 model families by over 7%. Notably, EyeCue can achieve an accuracy of over 70% across various driving scenarios (different road types, times of day, and weather conditions) with strong generalizability. These results highlight the importance of modeling gaze-context interactions and the effectiveness of cross-modal interaction modeling for multimodal cognitive distraction detection. Our codes and CogDrive dataset resources are available at https://github.com/langzhang2000/EyeCue.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
