Action Recognition Based on Sub-action Motion History Image and Static History Image

In this paper, we propose a robust and effective framework to largely improve the performance of human action recognition using depth maps. The key contribution is the proposition of the Sub-action Motion History Image (SMHI) and Static History Image (SHI) in a depth sequence. We evenly subdivide the normalized motion energy into a set of segments which corresponding frame indices are used to partition a video into different sub-actions segments. The Local Binary Patterns (LBP) descriptor is then computed from the SMHI and SHI for the representation of an action. We evaluate the proposed framework on MSR Action3D dataset. Experimental results indicate that the proposed approach outperforms the most of the art methods and demonstrate the effectiveness of the proposed approaches.