基于C3D的足球视频场景分类算法

足球视频整场比赛持续时间较长，许多视频内容并非广大观众的兴趣所在，因此足球视频场景分类成为了近几十年来研究界的一项重要课题，许多机器学习方法也被应用于这个课题上.本文提出的基于C3D （三维卷积神经网络）的足球视频场景分类算法，将三维卷积运用于足球视频领域，并通过实验验证了本文算法的可行性.本文实验的流程如下：首先，基于帧间差分法和徽标检测法检测法对足球视频场景切换进行检测，实现镜头分割.在此基础上，提取分割镜头的语义特征并将其进行标记，然后通过C3D对足球事件进行分类.本文将足球视频分为7类，分别为远镜头、中镜头、特写镜头、回放镜头、观众镜头、开场镜头及VAR （视频助理裁判）镜头.实验结果表明，该模型在足球视频数据集上的分类准确率为96%.

Soccer Video Scene Classification Algorithms Based on C3D

Football video lasts for a long time, and many video content is not the interest of audience. Therefore, football video scene classification has become an important research topic in recent decades, and many machine learning methods have also been applied to this topic. In this study, a soccer video scene classification algorithm based on 3D (three-dimensional) convolution neural network is proposed. The 3D convolution is applied to the field of soccer video, and the feasibility of this algorithm is verified by experiments. The flow of this experiment is as follows. Firstly, football video scene switching is detected based on frame difference method and logo detection method, and shot segmentation is realized. On this basis, the semantic features of shot segmentation are extracted and tagged, and then football events are classified by C3D. In this study, football videos are divided into seven categories: long shot, medium shot, close-up shot, playback shot, audience shot, opening shot, and VAR (Video Assistant Referee) shot. The experimental results show that the classification accuracy of the model is 96% on football video datasets.