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首页> 外文期刊>SIAM journal on applied dynamical systems >An Online Continuous Human Action Recognition Algorithm Based on the Kinect Sensor
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An Online Continuous Human Action Recognition Algorithm Based on the Kinect Sensor

机译：基于Kinect传感器的在线连续人体动作识别算法

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摘要

Continuous human action recognition (CHAR) is more practical in human-robot interactions. In this paper, an online CHAR algorithm is proposed based on skeletal data extracted from RGB-D images captured by Kinect sensors. Each human action is modeled by a sequence of key poses and atomic motions in a particular order. In order to extract key poses and atomic motions, feature sequences are divided into pose feature segments and motion feature segments, by use of the online segmentation method based on potential differences of features. Likelihood probabilities that each feature segment can be labeled as the extracted key poses or atomic motions, are computed in the online model matching process. An online classification method with variable-length maximal entropy Markov model (MEMM) is performed based on the likelihood probabilities, for recognizing continuous human actions. The variable-length MEMM method ensures the effectiveness and efficiency of the proposed CHAR method. Compared with the published CHAR methods, the proposed algorithm does not need to detect the start and end points of each human action in advance. The experimental results on public datasets show that the proposed algorithm is effective and highly-efficient for recognizing continuous human actions.
机译：连续人体行动识别（Char）在人机交互中更实用。本文基于从Kinect传感器捕获的RGB-D图像提取的骨架数据提出了一种在线CHAR算法。每个人类的行为通过特定顺序的一系列关键姿势和原子动作进行建模。为了提取密钥姿势和原子动作，通过基于特征潜在差异的在线分割方法，分为特征段和运动特征段的特征序列被分成姿势特征段和运动特征段。每个特征段可以被标记为提取的密钥姿势或原子动作的似然概率在在线模型匹配过程中计算。基于似然概率执行具有可变长度最大熵的在线分类方法（MEMM），用于识别持续的人类动作。可变长度MEMM方法确保所提出的CHAR方法的有效性和效率。与已发表的CHAR方法相比，所提出的算法不需要预先检测每个人类行动的开始和终点。公共数据集上的实验结果表明，该算法是有效且高度效率，可识别持续的人类行为。

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