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计算机工程 ›› 2008, Vol. 34 ›› Issue (19): 230-232,. doi: 10.3969/j.issn.1000-3428.2008.19.078

• 多媒体技术及应用 • 上一篇    下一篇

体育视频中的运动员检测与跟踪

吴海松1,2,华庆一1,2,李光俊1,2,沈 婧1   

  1. (1. 西北大学信息学院计算机科学系,西安 710069;2. 中国科学院计算机科学国家重点实验室,北京 100080)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-10-05 发布日期:2008-10-05

Player Detection and Tracking in Sports Videos

WU Hai-song1,2, HUA Qing-yi1,2, LI Guang-jun1,2, SHEN Jing1   

  1. (1. Department of Computer Science, Institute of Information Science, Northwest University, Xi’an 710069; 2. State Key Laboratory of Computer Science, Chinese Academy of Sciences, Beijing 100080)
  • Received:1900-01-01 Revised:1900-01-01 Online:2008-10-05 Published:2008-10-05

摘要: 利用自适应高斯混合模型对视频图像进行建模,从图像序列中获取背景图像并提取运动区域,利用像素的颜色信息从背景图像中提取绿色球场。为提高运动员检测的准确度,利用纹理相似性度量方法消除运动区域中的阴影,用形态学方法消除区域内的裂缝,根据球场信息去除球场外的噪声。改进了CamShift算法,并应用该算法对运动员进行跟踪。

关键词: 自适应高斯混合模型, 球场检测, 运动员检测, 运动员跟踪, CamShift算法

Abstract: This paper models each of the video frames as an adaptive Gaussian Mixture Model(GMM), and uses this model to extract the background image and to segment the moving regions. The color information is used to detect playfield based on the background image. In order to enhance the accuracy of the detected player regions, texture similarity measure is employed to remove the shadows, and a morphologic method is adopted to fill the gaps inside the player regions, noise is removed based on the playfield information. This paper improves the CamShift algorighm, which is used to track the players automatically.

Key words: adaptive GMM, playfield detection, player detection, player tracking, CamShift algorithm

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