With the development of technology, human motion capture data have been widely used in the fields of human–computer interaction, interactive entertainment, education, and medical treatment. As a problem in the field of computer vision, human motion recognition has become a key technology in somatosensory games, security protection, and multimedia information retrieval. Therefore, it is important to improve the recognition rate of human motion. Based on the above background, the purpose of this article is human motion recognition based on extreme learning machine. Based on the existing action feature descriptors, this article makes improvements to features and classifiers and performs experiments on the Microsoft model specific register (MSR)-Action3D data set and the Bonn University high density metal (HDM05) motion capture data set. Based on displacement covariance descriptor and direction histogram descriptor, this article described both combine to produce a new combination; the description can statically reflect the joint position relevant information and at the same time, the change information dynamically reflects the joint position, uses the extreme learning machine for classification, and gets better recognition result. The experimental results show that the combined descriptor and extreme learning machine recognition rate on these two data sets is significantly improved by about 3% compared with the existing methods.

中文翻译：

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基于极限学习机的人体运动识别

随着科技的发展，人体动作捕捉数据在人机交互、互动娱乐、教育、医疗等领域得到了广泛的应用。作为计算机视觉领域的一个难题，人体动作识别已经成为体感游戏、安全防护、多媒体信息检索等领域的关键技术。因此，提高人体运动的识别率非常重要。基于以上背景，本文的目的是基于极限学习机的人体动作识别。本文基于现有的动作特征描述符，对特征和分类器进行改进，并在微软模型特定寄存器（MSR）-Action3D数据集和波恩大学高密度金属（HDM05）运动捕捉数据集上进行实验。本文基于位移协方差描述符和方向直方图描述符，将两者结合产生新的组合；描述可以静态反映关节位置相关信息，同时变化信息动态反映关节位置，利用极限学习机进行分类，得到更好的识别结果。实验结果表明，结合描述符和极限学习机在这两个数据集上的识别率较现有方法显着提高了约3%。变化信息动态反映关节位置，利用极限学习机进行分类，得到更好的识别结果。实验结果表明，结合描述符和极限学习机在这两个数据集上的识别率较现有方法显着提高了约3%。变化信息动态反映关节位置，利用极限学习机进行分类，得到更好的识别结果。实验结果表明，结合描述符和极限学习机在这两个数据集上的识别率较现有方法显着提高了约3%。