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使用BiLSTM融合注意力机制的排球训练视频分类描述

¹School of Civil Aviation and Transportation, Zhejiang Yuying College of Vocational Technology, Hangzhou, 310018, China.

|August 15, 2024

概括

这项研究引入了用于排球视频分析的深度学习模型,在动作识别中实现了超过95%的准确性. BiLSTM-多模式注意力融合时间分类模型提高了教练和运动员的训练效率.

科学领域:

计算机科学计算机科学
运动科学运动科学运动科学
人工智能的人工智能

背景情况:

排球训练视频分析需要高效准确的方法.
深度学习为自动化体育视频内容分析提供了潜力.

研究的目的:

开发和验证一个深度学习模型来分类和描述排球训练视频.
提高体育视频分析的准确性和效率.

主要方法:

开发了一种双向长期短期记忆 (BiLSTM) 和注意力机制模型 (BiLSTM-MAFTC).
集成的多模式特征编码,BiLSTM用于时间建模,以及双注意模块用于特征相关性.
利用位置,模式,空间和通道数据进行全面分析.

主要成果:

在动作识别 (TOP-1和TOP-5指标) 中获得了超过95%的准确性.
展示了每视频0.04秒的识别速度.
成功处理了包括运动员运动和球轨迹在内的多式模式时间信息.

结论:

BiLSTM-MAFTC模型提供了精确的排球训练视频的分类和描述.
这种进步提高了教练和运动员的效率.
为更广泛的体育视频分析研究提供了宝贵的见解.

关键词:

注意力机制注意力机制这就是为什么BiLSTM.多式联运多式联运经常性的神经网络.排球运动视频排球运动视频

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