Human Action Recognition of Triangle Mesh Sequence Representation

Abstract

Considering the existing research works of human motion recognition are based on skeleton and video representations, a human motion classification method for triangle mesh sequence representation is proposed. Firstly, selecting the first frame of the triangle mesh sequence as the template model, the difference between each subsequent frame of the sequence and the template model is calculated by using the shape difference operator, which is expressed as the shape difference information tensor; Then, the shape difference information tensor is input into the deep network composed of two-dimensional convolution network and long short-term memory network to extract sequential action features for human action classification. The experimental results show that the classification accuracy of this method on human motion dataset AMASS reaches 100.00%.