2D第一人称手部姿态数据集基准测试
Benchmarking 2D Egocentric Hand Pose Datasets
AI总结：
针对2D第一人称手部姿态估计领域缺乏理想基准数据集的问题，提出包含特征分析、质量评估和模型验证的新型评估协议，发现H2O和GANerated Hands分别是最具潜力的真实与合成数据集。
摘要：
从第一人称视频中进行手部姿态估计在人机交互、辅助技术、活动识别和机器人学等多个领域具有广泛意义，因此成为极具研究价值的课题。现代机器学习模型的效能取决于训练所用数据的质量。因此，本工作致力于分析适用于2D手部姿态估计的前沿第一人称数据集。我们提出了一种新颖的数据集评估协议，该协议不仅包含对已声明数据集特征的分析和数据质量的评估，还通过评估前沿手部姿态估计模型来识别数据集的缺陷。我们的研究表明，尽管存在大量用于2D手部姿态估计的第一人称数据库，但大多数都是为特定用例定制的。目前尚无理想的基准数据集；不过，H2O和GANerated Hands数据集分别成为最具潜力的真实数据集和合成数据集。
英文摘要：
Hand pose estimation from egocentric video has broad implications across various domains, including human-computer interaction, assistive technologies, activity recognition, and robotics, making it a topic of significant research interest. The efficacy of modern machine learning models depends on the quality of data used for their training. Thus, this work is devoted to the analysis of state-of-the-art egocentric datasets suitable for 2D hand pose estimation. We propose a novel protocol for dataset evaluation, which encompasses not only the analysis of stated dataset characteristics and assessment of data quality, but also the identification of dataset shortcomings through the evaluation of state-of-the-art hand pose estimation models. Our study reveals that despite the availability of numerous egocentric databases intended for 2D hand pose estimation, the majority are tailored for specific use cases. There is no ideal benchmark dataset yet; however, H2O and GANerated Hands datasets emerge as the most promising real and synthetic datasets, respectively.
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。
