Abstract
In recent years, there has been increased interest in video summarization and automatic sports highlights generation. In this work, we introduce a new dataset, called SNOW, for umpire pose detection in the game of cricket. The proposed dataset is evaluated as a preliminary aid for developing systems to automatically generate cricket highlights. In cricket, the umpire has the authority to make important decisions about events on the field. The umpire signals important events using unique hand signals and gestures. We identify four such events for classification namely SIX, NO BALL, OUT and WIDE based on detecting the pose of the umpire from the frames of a cricket video. Pre-trained convolutional neural networks such as Inception V3 and VGG19 net-works are selected as primary candidates for feature extraction. The results are obtained using a linear SVM classifier. The highest classification performance was achieved for the SVM trained on features extracted from the VGG19 network. The preliminary results suggest that the proposed system is an effective solution for the application of cricket highlights generation.
Abstract (translated)
近年来，人们对视频摘要和自动体育精彩集合的兴趣日益增加。在这项工作中，我们引入了一个名为SNOW的新数据集，用于在板球比赛中进行裁判姿势检测。建议的数据集被评估为开发系统以自动生成板球亮点的初步辅助。在板球比赛中，裁判员有权对场上的事件做出重要决定。裁判使用独特的手势和手势发出重要事件的信号。我们基于从板球视频的帧中检测裁判的姿势来识别用于分类的四个这样的事件，即SIX，NO BALL，OUT和WIDE。预先训练的卷积神经网络如Inception V3和VGG19网络被选择作为特征提取的主要候选者。使用线性SVM分类器获得结果。针对从VGG19网络提取的特征训练的SVM实现了最高的分类性能。初步结果表明，所提出的系统是一种有效的解决方案，适用于板球高潮的产生。
URL
https://arxiv.org/abs/1809.06217
