As human action is a spatial–temporal process, modern action recognition research has focused on exploring more effective motion representations, rather than only taking human poses as input. To better model a motion pattern, in this paper, we exploit the rotation information to depict the spatial–temporal variation, thus enhancing the dynamic appearance, as well as forming a complementary component with the static coordinates of the joints. Specifically, we design to represent the movement of human body with joint units, consisting of performing regrouping human joints together with the adjacent two bones. Therefore, the rotation descriptors reduce the impact from the static values while focus on the dynamic movement. The proposed general features can be simply applied to existing CNN-based action recognition methods. The experimental results performed on NTU-RGB+D and ICL First Person Handpose datasets demonstrate the advantages of the proposed method.

由于人类动作是一个时空过程，因此现代动作识别研究的重点是探索更有效的动作表示，而不仅仅是以人类的姿势作为输入。为了更好地模拟运动模式，在本文中，我们利用旋转信息来描述时空变化，从而增强了动态外观，并与关节的静态坐标形成了互补分量。具体来说，我们设计用关节单元来代表人体的运动，包括执行将人体关节与相邻的两个骨骼重新组合在一起的操作。因此，旋转描述符减少了静态值的影响，同时专注于动态运动。所提出的一般特征可以简单地应用于现有的基于CNN的动作识别方法。