Video Image Information Mining Algorithms for Badminton on-the-Spot Tactics Analysis

Abstract

Due to the changes in the camera angle or the working direction of the athlete in the sports video, this method cannot perform an effective regional analysis. Hence, the efficient analysis of images is essential for the action recognition, hence, this paper studies the video image information mining algorithms for badminton on-the-spot tactics analysis. The designed system contains the image segmentation, action recognition and the badminton on-the-spot tactics analysis. To perform image segmentation, the FCM model is considered To perform action recognition, based on the obtained skeleton data, the model is trained by the action label data to classify it. An efficient action representation can capture both static and kinematic information. Then, the system is verified through the real-time data sets. The proposed model is proven to be efficient.