评估机器人交互中视线估计效果，发现实际误差达16.48厘米。
Gaze Estimation for Human-Robot Interaction: Analysis Using the NICO Platform
- 在共享工作区用NICO平台收集新标注数据集
- 最优模型在真实场景下中位误差为16.48厘米
- 提出将视线估计用于人机交互的优化建议
本文在共享工作区的人机交互场景中评估了当前的视线估计方法。我们引入了一个基于NICO机器人平台采集的新标注数据集，并对四种前沿视线估计模型进行了评估。结果表明，角度误差接近通用基准上的报告值，但以共享工作区内的距离衡量时，最佳模型的中位误差为16.48厘米，揭示了现有方法在实际应用中的局限性。最后，我们讨论了这些限制，并提出了在人机交互系统中有效整合视线估计这一模态的建议。
原文摘要 · Abstract (English)
This paper evaluates the current gaze estimation methods within an HRI context of a shared workspace scenario. We introduce a new, annotated dataset collected with the NICO robotic platform. We evaluate four state-of-the-art gaze estimation models. The evaluation shows that the angular errors are close to those reported on general-purpose benchmarks. However, when expressed in terms of distance in the shared workspace the best median error is 16.48 cm quantifying the practical limitations of current methods. We conclude by discussing these limitations and offering recommendations on how to best integrate gaze estimation as a modality in HRI systems.
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