Action description using point clouds

摘要

An action description method named as Motion History Point Cloud (MHPC) is proposed in this paper. MHPC compresses an action into a three-dimensional point cloud in which depth information is required. In MHPC, the spatial coordinate channels are used to record the motion foreground, and the color channels are used to record the temporal variation. Due to containing depth information, MHPC can depict an action more meticulous than Motion History Image (MHI). MHPC can serve as a pre-processed input for various classification methods, such as Bag of Words and Deep Learning. An action recognition scheme is provided as an application example of MHPC. In this scheme, Harris3D detector and Fast Point Feature Histogram (FPFH) are used to extract and describe features from MHPC. Then, Bag of Words and multiple classification Support Vector Machine (SVM) are used to do action recognition. The experiments show that rich features can be extracted from MHPC to support the subsequent action recognition even after downsampling. The feasibility and effectiveness of MHPC are also verified by comparing the above scheme with two similar methods.