  Human action recognition is an active research area in computer vision.
Although great process has been made, previous methods mostly recognize actions
based on depth data at only one scale, and thus they often neglect multi-scale
features that provide additional information action recognition in practical
application scenarios. In this paper, we present a novel framework focusing on
multi-scale motion information to recognize human actions from depth video
sequences. We propose a multi-scale feature map called Laplacian pyramid depth
motion images(LP-DMI). We employ depth motion images (DMI) as the templates to
generate the multi-scale static representation of actions. Then, we caculate
LP-DMI to enhance multi-scale dynamic information of motions and reduces
redundant static information in human bodies. We further extract the
multi-granularity descriptor called LP-DMI-HOG to provide more discriminative
features. Finally, we utilize extreme learning machine (ELM) for action
classification. The proposed method yeilds the recognition accuracy of 93.41%,
85.12%, 91.94% on public MSRAction3D dataset, UTD-MHAD and DHA dataset. Through
extensive experiments, we prove that our method outperforms state-of-the-art
benchmarks.
