• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2018 International Conference on Intelligent Systems and Computer Vision >End-to-end soccer video scene and event classification with deep transfer learning
【24h】

End-to-end soccer video scene and event classification with deep transfer learning

机译：具有深度迁移学习的端到端足球视频场景和事件分类

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Soccer video scene and event classification are two essential tasks for the soccer video semantic analysis and have attracted many interests of researchers because of their importance and practicability. However most proposed methods solve these two tasks separately. In order to solve two tasks at the same time and improve the efficiency of video processing, we treat them as one end-to-end classification task. We introduce a new Soccer Video Scene and Event Dataset (SVSED) with six categories from the scenes and events, which contains 600 video clips. Then, we show that frame features extracted from pretrained CNN model of different categories are separable in 3-D space. Finally, we construct a CNN model for the classification task and deep transfer learning method is used for optimizing classification task result considering relative small training datasets. We fine-tuned several state-of-art CNN models and achieves accuracy above 89% within several minutes training.
机译：足球视频场景和事件分类是足球视频语义分析的两个基本任务，由于其重要性和实用性，引起了研究者的广泛兴趣。但是，大多数提议的方法分别解决了这两个任务。为了同时解决两个任务并提高视频处理效率，我们将它们视为一项端到端分类任务。我们引入了一个新的足球视频场景和事件数据集（SVSED），其中包含来自场景和事件的六个类别，其中包含600个视频剪辑。然后，我们表明从不同类别的预训练CNN模型中提取的帧特征在3-D空间中是可分离的。最后，我们为分类任务构建了一个CNN模型，并考虑到相对较小的训练数据集，采用深度转移学习方法来优化分类任务结果。我们微调了几个最新的CNN模型，并在几分钟的训练中达到了89％以上的准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号