Human action recognition from RGB-D cameras has recently become one of the major fields of research. While accuracy improvement was given more importance in previous action/gesture recognition methods, there are opportunities to work on improving the computational efficiency too. This paper introduces an efficient dimensionality reduction technique and classification mechanism to recognize actions from depth motion map features. For our proposed work, a recently introduced technique called reduced basis decomposition (RBD) is employed, which manages faster dimensional reduction with its unique mechanism of generating compressed basis vectors. The RBD has an offline error-determination and an online approximation mechanism, and it is faster than PCA/SVD. For classification, this paper employs a Probabilistic Collaborative Representation Classifier (Pro-CRC). The recommended classifier works based on probability in connection with \({l_2}\)-regularization. The combined effect of the methods above helps in achieving the state-of-the-art efficiency. In the standard protocol tests carried out in the MSR-Action3D dataset, our proposed method achieved a considerable accuracy of 91.7% which is better than the currently efficient method. Further, our proposed method also proved its effectiveness in the challenging, subject-generic test with a reported accuracy of 89.64% and an average accuracy of 85.70% in the cross fixed tests which included 252 combinations of all the subjects without repetition.

中文翻译：

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基于减少的基础分解的RGB-D摄像机在线动作识别

RGB-D相机对人体动作的识别近来已成为研究的主要领域之一。尽管在以前的动作/手势识别方法中，提高准确性的重要性更高，但仍有许多机会可以提高计算效率。本文介绍了一种有效的降维技术和分类机制，可从深度运动图特征中识别动作。对于我们提出的工作，采用了最近引入的称为缩减基础分解（RBD）的技术，该技术通过其独特的生成压缩基础向量的机制来管理更快的尺寸缩减。RBD具有离线错误确定和在线近似机制，并且比PCA / SVD更快。对于分类，本文采用了概率协同表示分类器（Pro-CRC）。推荐的分类器基于与\（{l_2} \） -正规化。以上方法的综合效果有助于实现最新的效率。在MSR-Action3D数据集中执行的标准协议测试中，我们提出的方法达到了91.7％的可观精度，这比当前有效的方法要好。此外，我们提出的方法还证明了其在具有挑战性的受试者通用测试中的有效性，据报道的准确度为89.64％，在交叉固定测试中的平均准确度为85.70％，其中包括所有受试者的252种组合，没有重复。