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Human Activity Recognition Based on 3D Mesh MoSIFT Feature Descriptor

机译:基于3D Mesh MoSIFT特征描述符的人类活动识别

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The times of Big Data promotes increasingly higher demands for information processing. The rapid development of 3D digital capturing devices prompts the traditional behavior analysis towards fine motion recognition, such as hands and gesture. In this paper, a complete framework of 3D human activity recognition is presented for the behavior analysis of hands and gesture. First, the improved graph cuts method is introduced to hand segmentation and tracking. Then, combined with 3D geometric characteristics and human behavior prior information, 3D Mesh MoSIFT feature descriptor is proposed to represent the discriminant property of human activity. Simulation orthogonal matching pursuit (SOMP) is used to encode the visual code words. Experiments, based on a RGB-D video dataset and ChaLearn gesture dataset, show the improved accuracy of human activity recognition.
机译:大数据时代对信息处理提出了越来越高的要求。 3D数字捕捉设备的快速发展促使传统的行为分析朝着精细运动识别(例如手和手势)的方向发展。本文提出了一个完整的3D人类活动识别框架,用于手和手势的行为分析。首先,将改进的图割方法引入到手的分割和跟踪中。然后,结合3D几何特征和人类行为先验信息,提出了3D Mesh MoSIFT特征描述符来表示人类活动的判别性质。仿真正交匹配追踪(SOMP)用于对视觉代码字进行编码。基于RGB-D视频数据集和ChaLearn手势数据集的实验表明,人类活动识别的准确性有所提高。

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