Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which change significantly with viewpoint. In contrast, we directly process the pointclouds and propose a new technique for action recognition which is more robust to noise, action speed and viewpoint variations. Our technique consists of a novel descriptor and keypoint detection algorithm. The proposed descriptor is extracted at a point by encoding the Histogram of Oriented Principal Components (HOPC) within an adaptive spatio-temporal support volume around that point. Based on this descriptor, we present a novel method to detect Spatio-Temporal Key-Points (STKPs) in 3D pointcloud sequences. Experimental results show that the proposed descriptor and STKP detector outperform state-of-the-art algorithms on three benchmark human activity datasets. We also introduce a new multiview public dataset and show the robustness of our proposed method to viewpoint variations.

本文提出了一种新的3D动作识别技术，直接处理点云数据，并通过新的描述符和关键点检测算法提高了在噪声、动作速度和视角变化方面的稳健性。实验结果表明，该方法在人类活动数据库上的表现优于现有的算法，并引入了一个新的多视图公共数据集以展示我们的方法对视角变化的鲁棒性。

HOPC：三维点云的方向主成分直方图用于动作识别