Towards Deep Learning based Hand Keypoints Detection for Rapid Sequential Movements from RGB Images
Towards Deep Learning based Hand Keypoints Detection for Rapid Sequential Movements from RGB Images
复制标题
基于深度学习的手部关键点检测，用于 RGB 图像中的快速连续运动
DOI：
10.1145/3197768.3201538
复制
发表时间：
2018
期刊：
影响因子：
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通讯作者：
Athitsos, Vassilis
中科院分区：
文献类型：
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作者：
Gattupalli, Srujana;Babu, Ashwin Ramesh;Brady, James Robert;Makedon, Fillia;Athitsos, Vassilis
手部关键点检测和姿态估计在计算机视觉中有着广泛的应用，但在很多方面仍是一个尚未解决的问题。手部关键点检测的一个应用是通过观察受试者在涉及手指快速运动的物理任务中的表现来执行对受试者的认知评估。作为这项工作的一部分，我们引入了一个新的手部关键点基准数据集，该数据集包括专门用于认知行为监控的手势记录。我们探讨了手部关键点检测的最新方法，并在我们的数据集上对这些方法的性能进行了定量评估。在未来，这些结果和我们的数据集可以作为快速手指运动的手部关键点识别的有用基准。
Hand keypoints detection and pose estimation has numerous applications in computer vision, but it is still an unsolved problem in many aspects. An application of hand keypoints detection is in performing cognitive assessments of a subject by observing the performance of that subject in physical tasks involving rapid finger motion. As a part of this work, we introduce a novel hand keypoints benchmark dataset that consists of hand gestures recorded specifically for cognitive behavior monitoring. We explore the state of the art methods in hand keypoint detection and we provide quantitative evaluations for the performance of these methods on our dataset. In future, these results and our dataset can serve as a useful benchmark for hand keypoint recognition for rapid finger movements.
使用霍夫随机森林进行基于深度的手势分割
DOI：
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发表时间：
2016
期刊：
2016 3rd International Conference on Green Technology and Sustainable Development (GTSD)
影响因子：
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作者：
Wei;Ju;K. W. Lin
通讯作者：
K. W. Lin