In this paper, a new effective method was proposed to recognize human actions based on RGBD data sensed by a depth camera, namely Microsoft Kinect. Skeleton data extracted from depth images was utilized to generate 10 direction features which represent specific body parts and 11 position features which represent specific human joints. The fusion features composed of both was used to represent a human posture. An algorithm based on the difference level of adjacent postures was presented to select the key postures from an action. Finally, the action features, composed of the key postures' features, were classified and recognized by a multiclass Support Vector Machine. Our major contributions are proposing a new framework to recognize the users' actions and a simple and effective method to select the key postures. The recognition results in the KARD dataset and the Florence 3D Action dataset show that our approach significantly outperforms the compared methods.
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机译：本文提出了一种新的有效方法，即基于深度相机感应到的RGBD数据的人为动作识别方法，即Microsoft Kinect。从深度图像提取的骨骼数据被用来生成代表特定身体部位的10个方向特征和代表特定人体关节的11个位置特征。由两者组成的融合特征被用来代表人类的姿势。提出了一种基于相邻姿势差异水平的算法，从动作中选择关键姿势。最后，由关键姿势特征组成的动作特征通过多类支持向量机进行分类和识别。我们的主要贡献是提出了一个新的框架来识别用户的行为，并提出了一种简单有效的方法来选择关键姿势。 KARD数据集和Florence 3D Action数据集的识别结果表明，我们的方法明显优于所比较的方法。
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