Human action recognition based on skeleton features

Based on human bone joints, skeleton information has clear and simple
 features and is not easily affected by appearance factors. In this paper, an
 improved feature of Gist, ExGist, is proposed to describe the skeleton
 information of human bone joints for human action recognition. The joint
 coordinates are extracted by using OpenPose and the thermodynamic diagram,
 and ExGist is used for feature extraction. The advantage of ExGist is that
 it can effectively characterize the local and global features of skeleton
 information while maintaining the original advantages of Gist feature.
 Compared with Gist, ExGist achieves better results on different classifiers.
 Additionally, compared with C3D and APTNet, our model also obtains better
 results with an accuracy rate of 89.2%.