OpenPose’s Evaluation in The Video Traditional Martial Arts Presentation

Preserving, maintaining and teaching traditional martial arts are very important activities in social life. That helps individuals preserve national culture, exercise and self-defense for people. However, traditional martial arts have many different postures and activities of the body and body parts. The problem of estimating the actions of the human body still has many challenges, such as accuracy, obscurity, and so forth. Especially is 3-D human pose estimation. In this paper, we propose using Convolutional Neural Network (CNN) for estimating key points and joints of actions in traditional martial postures in the 2-D space and then projecting the results to the 3-D space and apply the measurements (length of joints, deviation angle of joints, deviation distance of key points) for evaluating pose estimation. The CNN model that we use is CPM (Convolutional Pose Machine), since then there is a comparative study proposed when CPM is trained on the classic MSCOCO Keypoints Challenge dataset [1] and Human3.6m [2], the results were evaluated on the Martial Arts, Dancing and Sports dataset of Zhang et al. [3]. The quantitatively results evaluated and published.