This paper proposes a framework for recognizing human actions from depth video sequences by designing a novel feature descriptor based on Depth Motion Maps (DMMs), Contour let Transform (CT) and Histogram of Oriented Gradients (HOGs). First, CT is implemented on the generated DMMs of a depth video sequence and then HOGs are computed for each contour let sub-band. Finally, the concatenation of these HOG features is used as a feature descriptor for the depth video sequence. With this new feature descriptor, the l2-regularized collaborative representation classifier is utilized to recognize human actions. The experimental results on Microsoft Research Action3D dataset demonstrate that our proposed method can achieve the state-of-the-art performance for human activity recognition due to the precise feature extraction of contour let transform on the DMMs.
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机译：本文提出了一种通过基于深度运动映射（DMMS）的新颖特征描述符来识别来自深度视频序列的人类动作的框架，轮廓让变换（CT）和面向梯度（HOGS）的直方图。首先，CT在深度视频序列的生成DMMS上实现，然后针对每个轮廓计算HOGS，使子带。最后，将这些猪的串联用作深度视频序列的特征描述符。使用此新功能描述符，利用L2正则化协同表示分类器来识别人类的行为。 Microsoft Research Action3D DataSet上的实验结果表明，由于轮廓的精确特征提取，我们所提出的方法可以实现人类活动识别的最先进的性能，以便在DMMS上变换。
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