A Real-time Basketball Action Recognition based on Machine Learning Algorithm in Multi-View Environment

• Main Authors: Wei-Yuan Kuo, 郭瑋元
• Other Authors: Pao-Chi Chang
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/et723c

碩士 === 國立中央大學 === 通訊工程學系 === 105 === Human action recognition has been an important research in computer vision and computer graphics. It is widely used in entertainment, sports, medical applications and surveillance system. The traditional motion capture equipment is not usually affordable for normal developer. With the reasonable price of Kinect camera, low-cost human motion recognition becomes possible. In this paper, we use multiple Kinect sensors and Kinect SDK as the tool to build our human action recognition system. This solves the problem of action recognition equipment costs. Using multiple Kinect cameras to solve the judging and correction error problems (such as self-occlusion and image noise...etc.) and using machine learning method to classified our features, it can make our recognition result with higher performance. In our methods, we also have a detection of basketball to prevent that the subject is without ball, it makes our works more reasonable. Above of all, this paper have the action recognition rate to be more than 90% in real-time usage from three of the trained behaviors, i.e. right-hand dribble, left-hand dribble, and shooting behaviors.