Spatio-temporal pyramid cuboid matching for action recognition using depth maps

This paper presents a novel framework, spatio-temporal pyramid cuboid matching (STPCM), which is designed to recognize human actions from sequences captured by depth cameras. A depth sequence is partitioned into sub-volumes and represented using pyramid motion history templates (PMHT), which maintain the multi-scale 3D motion and shape information along the temporal direction. In order to capture the spatial information of PMHT, each projected plane from PMHT is subdivided into pyramid spatio-temporal grids. We then propose a novel cuboid fusion scheme to combine spatial dependent grids from projected planes to construct pyramid cuboids that consider the 3D spatial locations in conjunction with temporal information. In the experiments, we evaluate the proposed framework on three public benchmark datasets. Experimental results demonstrate that the proposed method achieves state-of-the-art performance.