摘要翻译：
近十年来，运动捕捉技术在电影特效、控制游戏和机器人、康复系统、动画等领域的应用越来越广泛。目前的人体运动捕捉技术使用标记、结构化环境和专用环境中的高分辨率摄像机。由于人体运动的快速性，肘部角度估计一直是人体运动捕捉系统中最困难的问题。本文以肘角估计为研究对象，提出了一种新颖的、无标记的、低成本的解决方案，该方案利用部分亲和力场，利用RGB摄像机实时估计肘角。我们招募了五（5）名参与者进行杯对嘴运动，同时用RGB相机和微软Kinect测量角度。实验结果表明，与微软Kinect相比，无标记和高性价比的RGB相机在矢状面和冠状面的均方根误差中值分别为3.06{deg}和0.95{deg}。
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英文标题：
《Real Time Elbow Angle Estimation Using Single RGB Camera》
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作者：
Muhammad Yahya, Jawad Ali Shah, Arif Warsi, Kushsairy Kadir, Sheroz
  Khan, M Izani
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  The use of motion capture has increased from last decade in a varied spectrum of applications like film special effects, controlling games and robots, rehabilitation system, animations etc. The current human motion capture techniques use markers, structured environment, and high resolution cameras in a dedicated environment. Because of rapid movement, elbow angle estimation is observed as the most difficult problem in human motion capture system. In this paper, we take elbow angle estimation as our research subject and propose a novel, markerless and cost-effective solution that uses RGB camera for estimating elbow angle in real time using part affinity field. We have recruited five (5) participants to perform cup to mouth movement and at the same time measured the angle by both RGB camera and Microsoft Kinect. The experimental results illustrate that markerless and cost-effective RGB camera has a median RMS errors of 3.06{\deg} and 0.95{\deg} in sagittal and coronal plane respectively as compared to Microsoft Kinect.
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PDF链接：
https://arxiv.org/pdf/1808.07017