Cricket highlight generation is the process of summarizing a full-length video to a shortened form which should preserve the important moments present in the original video. In this paper, a new approach has been proposed for recognizing the key events and summarization. Audio features are initially used for extracting the excitement clips. Then, the important events like replay, players, umpires, spectators, and players gathering are extracted from each clip. Here, a hybrid deep neural network with Emperor Penguin optimization (HDNN-EPO) is proposed for labeling the excitement concepts presented in the cricket video based on the observed events automatically. These labeled concepts are then selected based on the importance degree and concatenated in temporal order to form highlights. The efficiency of the proposed method is proved through the experimental results and it outperforms the other existing approaches. Also, the extracted highlights have been compared with the manually-generated highlights by the sports television channel.

中文翻译：

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帝企鹅优化了板球比赛亮点生成的事件识别和总结

板球精彩片段生成是将全长视频总结为缩短形式的过程，该过程应保留原始视频中存在的重要时刻。在本文中，提出了一种新的方法来识别关键事件和总结。音频特征最初用于提取兴奋片段。然后，从每个片段中提取重播、球员、裁判、观众和球员聚集等重要事件。在这里，提出了一种具有帝企鹅优化（HDNN-EPO）的混合深度神经网络，用于根据观察到的事件自动标记板球视频中呈现的兴奋概念。然后根据重要性程度选择这些标记的概念，并按时间顺序连接以形成亮点。通过实验结果证明了所提出方法的有效性，并且它优于其他现有方法。此外，提取的精彩集锦已与体育电视频道手动生成的精彩集锦进行了比较。