体育视频中羽毛球运动员的动作识别

Badminton Athlete's Movement Identification in Sports Videos

在体育视频通常存在像质量不佳、非静态视频以及图像分辨率低等问题的背景下,基于支持向量机(SVM)和光流分析,提出了一种识别体育视频中羽毛球运动员运动的新方法。该方法从视觉分析的角度出发,采用一种局部分析的网格分类方法提取动作识别的描述符算子。与传统的光流分析思路不同,该方法将目标区域中的光流矢量作为一种在移动模式下空间分布信息,并且使用SVM作为模型分类器和时间序列图像识别策略,对羽毛球运动员的三种典型挥拍动作进行识别。通过实验表明,与现有基于动作姿态特征重构的识别方法相比,本文所提出的识别技术具有更好的挥拍动作分类识别效果。

In the context of sports videos often have problems such as poor quality,non-static video,and low image resolution,this paper proposes a method to identify the sports of badminton players in sports videos based on Support Vector Machine(SVM)and optical flow analysis.From the perspective of visual analysis,this method uses a local analysis grid classification method to extract the descriptor of the motion recognition descriptor.With traditional optical flow analysis methods,this method targets area of the optical flow vector as a mobile mode space distribution information,and using SVM as classifier model and time sequence image recognition strategy,three typical of badminton players swing for identification.Experiments show that compared with the existing recognition methods based on feature reconstruction of motion gestures,the recognition technology presented in this paper has better effect of classification and recognition of swing motions.