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Automatic Soccer Video Event Detection Based on a Deep Neural Network Combined CNN and RNN

机译：基于CNN和RNN结合的深度神经网络的足球视频事件自动检测

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摘要

Soccer video semantic analysis has attracted a lot of researchers in the last few years. Many methods of machine learning have been applied to this task and have achieved some positive results, but the neural network method has not yet been used to this task from now. Taking into account the advantages of Convolution Neural Network(CNN) in fully exploiting features and the ability of Recurrent Neural Network(RNN) in dealing with the temporal relation, we construct a deep neural network to detect soccer video event in this paper. First we determine the soccer video event boundary which we used Play-Break(PB) segment by the traditional method. Then we extract the semantic features of key frames from PB segment by pre-trained CNN, and at last use RNN to map the semantic features of PB to soccer event types, including goal, goal attempt, card and corner. Because there is no suitable and effective dataset, we classify soccer frame images into nine categories according to their different semantic views and then construct a dataset called Soccer Semantic Image Dataset(SSID) for training CNN. The sufficient experiments evaluated on 30 soccer match videos demonstrate the effectiveness of our method than state-of-art methods.
机译：足球视频语义分析在最近几年吸引了很多研究人员。机器学习的许多方法已应用于此任务并取得了一些积极的成果，但是从现在开始，尚未将神经网络方法用于此任务。考虑到卷积神经网络（CNN）在充分利用特征方面的优势以及递归神经网络（RNN）处理时间关系的能力，我们构建了一个深度神经网络来检测足球视频事件。首先，我们通过传统方法确定使用Play-Break（PB）片段的足球视频事件边界。然后通过预训练的CNN从PB段中提取关键帧的语义特征，最后利用RNN将PB的语义特征映射到足球事件类型，包括进球，射门得分，射门得分和角球。由于没有合适且有效的数据集，因此我们根据足球框架图像的不同语义视图将其分为九类，然后构建一个名为“足球语义图像数据集”（SSID）的数据集来训练CNN。在30个足球比赛视频上评估的足够实验证明了我们的方法比最新方法的有效性。

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