3-D Human Pose Estimation by Convolutional Neural Network in the Video Traditional Martial Arts Presentation

Abstract

Preservation and maintenance of traditional martial arts and teaching martial arts are very important activities in social life. It helps preserving national culture, train health, and self-defense for people. However, traditional martial arts have many different postures and activities of the body and body parts. In this paper, we are proposed using deep learning with Convolutional Neural Network (CNN) for estimating key points and joints of actions in traditional martial arts postures and proposed the evaluation methods. The training set has been learned on the 2016 MSCOCO keypoints challenge classic database, the results are evaluated on 14 videos of traditional martial art performances with complicated postures. The estimated results are high and published. In particular, we present the results of estimating key points and joints in 3-D space to support the construction of a traditional martial arts conservation and teaching application.