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A New Method for Shot Identification in Basketball Video

机译:篮球视频镜头识别的新方法

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摘要

This paper presents semantic-based shot event identification. Based on Dynamic Bayesian Network (DBN), the gap between low-lever features and high-lever semantic will be resolve. We apply the mean-shift algorithm and Kalman filter to identify and track the ball and SURF(Speed up Robust Features) to find the basketball hoop. At last the DBN is applied to identify the shot events. Experimental results have shown our proposed method is effective for basketball event detection.
机译:本文提出了基于语义的镜头事件识别。基于动态贝叶斯网络(DBN),将解决低杠杆特征与高杠杆语义之间的鸿沟。我们应用均值漂移算法和卡尔曼滤波器来识别和跟踪球,并使用SURF(加速鲁棒特征)找到篮球篮。最后,使用DBN来识别击球事件。实验结果表明我们提出的方法对于篮球比赛事件检测是有效的。

著录项

  • 来源
    《Journal of software》 |2011年第8期|p.1468-1475|共8页
  • 作者

    Yun Liu; Xueying Liu; Chao Huang;

  • 作者单位

    College of Information Science and Technology of Qingdao University of Science and Technology, Shandong province, China;

    College of Information Science and Technology of Qingdao University of Science and Technology, Shandong province, China;

    College of Information Science and Technology of Qingdao University of Science and Technology, Shandong province, China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    mean-shift; shot identification; SURF; DBN;

    机译:平均移动镜头识别;冲浪;德班;

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