Automatic Badminton Action Recognition Using RGB-D Sensor

Abstract

This paper presents a method to recognize badminton action from depth map sequences acquired by Microsoft Kinect sensor. Badminton is one of Malaysia’s most popular, but there is still lack of research on action recognition focusing on this sport. In this research, bone orientation details of badminton players are computed and extracted in order to form a bag of quaternions feature vectors. After conversion to log-covariance matrix, the system is trained and the badminton actions are classified by a support vector machine classifier. Our experimental dataset of depth map sequences composed of 300 badminton action samples of 10 badminton actions performed by six badminton players. The dataset varies in terms of human body size, clothes, speed, and gender. Experimental result has shown that nearly 92% of average recognition accuracy (ARA) was achieved in inter-class leave one sample out cross validation test. At the same time, 86% of ARA was achieved in inter-class cross subject validation test.

An overview of Human Action Recognition in sports based on Computer Vision

“…In [ 86 ], researchers recognized ten badminton actions from 300 depth map sequences acquired by the Microsoft Kinect sensor. Skeleton orientation details of badminton players were computed and extracted to form a bag of quaternions feature vectors.…”