A review of action recognition methods based on skeleton data

Human action recognition task has gradually become one of the most popular research topics in the field of computer vision. In this task, the action recognition based on human bone data is the most attractive. The human skeleton data contains a lot of correlation information and hidden information, so this kind of task model can well extract the difference characteristics and human movement trajectory, etc., which plays a key role in improving the accuracy of the task. At the same time, the skeleton-based action recognition algorithm based on CNN, RNN, GCN, LSTM and other basic models improves the task capability from the aspects of accuracy, computational complexity and so on. From this perspective, this paper reviews the deep learning models and variants of action recognition based on skeleton data, and also summarizes the bone information datasets used for such tasks.