论文标题
肌肉视觉：实时关键点的体育锻炼分类
Muscle Vision: Real Time Keypoint Based Pose Classification of Physical Exercises
论文作者
论文摘要
机器学习技术的最新进展已使许多常见任务的高度便携式和性能模型，尤其是在图像识别方面。从视频中推断出的一个新兴领域，即3D人类姿势识别，现在已经促进具有足够强大输出的实时软件应用程序，以支持下游机器学习任务。在这项工作中，我们提出了一个新的机器学习管道和Web界面，该管道和Web界面在实时视频提要上执行人体姿势识别，以检测何时进行常见的练习并相应地对其进行分类。我们提出了一个模型接口，能够实时显示分类结果的网络摄像头输入。我们的主要贡献包括基于关键和时间序列的轻巧方法，用于分类选定的健身练习集和基于Web的软件应用程序，以实时获得和可视化结果。
Recent advances in machine learning technology have enabled highly portable and performant models for many common tasks, especially in image recognition. One emerging field, 3D human pose recognition extrapolated from video, has now advanced to the point of enabling real-time software applications with robust enough output to support downstream machine learning tasks. In this work we propose a new machine learning pipeline and web interface that performs human pose recognition on a live video feed to detect when common exercises are performed and classify them accordingly. We present a model interface capable of webcam input with live display of classification results. Our main contributions include a keypoint and time series based lightweight approach for classifying a selected set of fitness exercises and a web-based software application for obtaining and visualizing the results in real time.
