Abstract des Autors

In sports science, two widely used approaches to perform movement recognition and analysis are through manual annotation of sports video and physical body marker attached to athlete’s body. The use of physical body markers, however, requires expertise on visual annotation which is obviously time-consuming and inconvenient for the athletes. Badminton is one of Malaysia’s most popular sports but there is still a lack of scientific research on movement recognition and analysis focusing on this sport. Therefore, in this paper, a novel lossless compact view invariant compression technique with a dynamic time warping algorithm is proposed to cater for both badminton movement recognition and analysis frameworks. Our experimental dataset of depth map sequences composed of 10 types of badminton movements with a total of 600 samples performed by 20 badminton players. The dataset varies in terms of viewpoints, human body size, clothes, speed, and gender. Experimental results revealed that nearly 95% of average recognition accuracy was accomplished for badminton movement recognition framework. In addition, badminton movement can be analyzed in detail and compared by using the movement analysis framework. The present research will be beneficial to sport scientists, badminton coaches, and potentially useful in enhancing the performance of badminton players.