Summarization is important in sports video analysis; it gives a more compact and interesting representation of content. The automatic cricket video summarization is more challenging as it contains several rules and longer match duration. In this research, a hybrid machine learning approach is proposed to summarize cricket video. It analyzes the excitement, object, and event-based features for the detection of key events from the cricket video. First, the audio is analyzed for the extraction of the exciting clips by using an adaptive threshold, speech-to-text framework, and Stacked Gated Recurrent Neural Network with Attention Module (SGRNN-AM). Then, the scenes of each exciting clip are classified with a new Hybrid Rotation Forest Deep Belief Network (HRF-DBN). Next, the characters and action features are extracted from the scorecard region of each key frame and umpire frames of exciting clips. Finally, SGRNN-AM model is used to detect key events including fours, sixes, and wickets. The accuracy of the proposed SGRNN-AM video summarization model is increased with an attention module in the hidden outputs of Gated Recurrent Unit (GRU) for selecting the significant features. The performance of the suggested technique has been improved on various collections of cricket videos. It achieved a precision of \(96.82\ \%\) and an accuracy of \(96.32\%\) that proves its effectiveness.

总结在体育视频分析中很重要；它提供了更紧凑和有趣的内容表示形式。自动板球视频摘要更具挑战性，因为它包含多个规则和更长的比赛持续时间。在这项研究中，提出了一种混合机器学习方法来总结板球视频。它分析了兴奋，基于对象和基于事件的功能，以便从板球视频中检测关键事件。首先，通过使用自适应阈值，语音到文本框架以及带有注意模块的堆叠门控递归神经网络（SGRNN-AM），对音频进行分析，以提取令人兴奋的剪辑。然后，使用新的混合旋转森林深层信任网络（HRF-DBN）对每个令人兴奋的剪辑的场景进行分类。下一个，从每个关键帧的记分卡区域和令人兴奋的剪辑的裁判帧中提取角色和动作特征。最后，使用SGRNN-AM模型来检测关键事件，包括四，六，和小门。通过在选通循环单元（GRU）的隐藏输出中使用注意模块来选择重要特征，可以提高建议的SGRNN-AM视频摘要模型的准确性。各种板球视频集都提高了建议技术的性能。它达到了 通过在选通循环单元（GRU）的隐藏输出中使用注意模块来选择重要特征，可以提高建议的SGRNN-AM视频摘要模型的准确性。各种板球视频集都提高了建议技术的性能。它达到了 通过在选通循环单元（GRU）的隐藏输出中使用注意模块来选择重要特征，可以提高建议的SGRNN-AM视频摘要模型的准确性。各种板球视频集都提高了建议技术的性能。它达到了\（96.82 \ \％\）和\（96.32 \％\）的准确性证明了其有效性。