Human motion recognition is a branch of computer vision research and is widely used in fields like interactive entertainment. Most research work focuses on human motion recognition methods based on traditional video streams. Traditional RGB video contains rich colors, edges, and other information, but due to complex background, variable illumination, occlusion, viewing angle changes, and other factors, the accuracy of motion recognition algorithms is not high. For the problems, this article puts forward human motion recognition based on extreme learning machine (ELM). ELM uses the randomly calculated implicit network layer parameters for network training, which greatly reduces the time spent on network training and reduces computational complexity. In this article, the interframe difference method is used to detect the motion region, and then, the HOG3D feature descriptor is used for feature extraction. Finally, ELM is used for classification and recognition. The results imply that the method proposed here has achieved good results in human motion recognition.

人体运动识别是计算机视觉研究的一个分支，广泛用于交互式娱乐等领域。大多数研究工作集中在基于传统视频流的人体运动识别方法上。传统的RGB视频包含丰富的颜色，边缘和其他信息，但是由于背景复杂，照明变化，遮挡，视角变化等因素，运动识别算法的准确性并不高。针对这些问题，本文提出了基于极限学习机（ELM）的人体运动识别。ELM使用随机计算的隐式网络层参数进行网络训练，这大大减少了在网络训练上花费的时间，并降低了计算复杂度。在本文中，使用帧间差分方法检测运动区域，然后，HOG3D特征描述符用于特征提取。最后，ELM用于分类和识别。结果表明，本文提出的方法在人体运动识别中取得了良好的效果。