Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems.
In this work, we propose to use a new class of models known as Temporal Convolutional Neural Networks (TCN) for 3D human action recognition.
Recent approaches in depth-based human activity analysis achieved outstanding performance and proved the effectiveness of 3D representation for classification of action classes.
#4 best model for Skeleton Based Action Recognition on Varying-view RGB-D Action-Skeleton
The proposed representation has the advantage of combining the use of reference joints and a tree structure skeleton.
Due to the availability of large-scale skeleton datasets, 3D human action recognition has recently called the attention of computer vision community.
The proposed method achieved state-of-the-art performance on NTU RGB+D dataset for 3D human action analysis.