论文标题
行列，包括征素，监督本征和渔具，以进行3D动作识别
Roweisposes, Including Eigenposes, Supervised Eigenposes, and Fisherposes, for 3D Action Recognition
论文作者
论文摘要
人类行动识别是计算机视觉和机器学习的重要领域之一。尽管已经提出了各种方法以识别3D动作识别，其中一些是基本的，有些是使用深度学习的，但人们对基于广义特征值问题的基本方法的需求被视为行动识别。这种需求特别感知，因为在特征和渔夫等面部识别领域具有类似的基本方法。在本文中，我们提出了使用Roweis判别分析进行广义子空间学习的Roweisposes。该方法包括渔具，本征，监督的本征和双重监督算术作为特殊情况。 Roweisposes是一个无限数量的动作重新处理方法的家族，该方法学习了嵌入身体构成的歧视性子空间。在TST，UTKINECT和UCFKINECT数据集上进行的实验验证了提出的动作识别方法的有效性。
Human action recognition is one of the important fields of computer vision and machine learning. Although various methods have been proposed for 3D action recognition, some of which are basic and some use deep learning, the need of basic methods based on generalized eigenvalue problem is sensed for action recognition. This need is especially sensed because of having similar basic methods in the field of face recognition such as eigenfaces and Fisherfaces. In this paper, we propose Roweisposes which uses Roweis discriminant analysis for generalized subspace learning. This method includes Fisherposes, eigenposes, supervised eigenposes, and double supervised eigenposes as its special cases. Roweisposes is a family of infinite number of action recongition methods which learn a discriminative subspace for embedding the body poses. Experiments on the TST, UTKinect, and UCFKinect datasets verify the effectiveness of the proposed method for action recognition.
