基于多媒体网络技术的篮球动态运动行为识别算法

Xiaoyu Wu, Yumei Xue
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引用次数: 0

摘要

为了实现基于计算机视觉技术的动态比赛训练场景下篮球动态运动行为的识别,结合视频数据分析,提高篮球动态运动行为的规范化程度,本文提出了一种基于多媒体网络的动态比赛训练场景下篮球动态运动行为的识别方法。采用光流采集的方法,在比赛和训练的动态场景中采集篮球动态运动行为转换过程中的RGB图像数据。对采集到的比赛训练动态场景中篮球动态运动的光流场进行规范特征提取,并采用多媒体网络技术视频监督学习的方法对比赛训练动态场景中篮球动态运动的视频参数进行分析。在动态篮球视觉和光流场变化参数约束下,采用二自由度动态参数模型参数学习提取篮球动态运动序列的时空特征。结合特征提取、特征融合和特征分类技术,分析了篮球动态运动行为转化过程中的光流轨迹参数,结合场景属性和时空兴趣点分布,实现了比赛训练动态场景中篮球动态运动行为的动态参数识别。仿真结果表明,该方法能够有效识别比赛和训练动态场景中篮球动态运动行为的静态和动态特征,提高了篮球动态运动行为识别的维度,从而有效实现了复杂背景和多人交互下篮球动态运动行为的准确检测和识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recognition Algorithm of Basketball Dynamic Movement Behavior Based on Multimedia Network Technology
In order to realize the recognition of basketball's dynamic sports behavior in the dynamic scene of competition and training based on computer vision technology, combined with video data analysis, and improve the standardization of basketball's dynamic sports behavior, this paper puts forward a recognition method of basketball's dynamic sports behavior in the dynamic scene of competition and training based on multimedia network, and adopts the method of optical flow acquisition to collect RGB image data during the conversion process of basketball's dynamic sports behavior in the dynamic scene of competition and training. The collected optical flow field of basketball dynamic motion in the dynamic scene of competition and training is extracted with normative features, and the video parameters of basketball dynamic motion in the dynamic scene of competition and training are analyzed with the method of multimedia network technology video supervised learning. Under the constraint of dynamic basketball vision and optical flow field changing parameters, the temporal and spatial features extracted from basketball dynamic motion sequence are extracted by two-degree-of-freedom dynamic parameter model parameter learning. Combining the techniques of feature extraction, feature fusion and feature classification, this paper analyzes the optical flow trajectory parameters in the process of basketball dynamic movement behavior transformation, and realizes the dynamic parameter identification of basketball dynamic movement behavior in the dynamic scene of competition and training by combining the scene attributes and the distribution of time and space points of interest. The simulation results show that this method can effectively recognize the static and dynamic characteristics of basketball dynamic movement behavior in the dynamic scene of competition and training, and improve the dimension of basketball dynamic movement behavior recognition, thus effectively realizing the accurate detection and recognition of basketball dynamic movement behavior under complex background and multi-person interaction.
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