期刊文献+

基于象素级别和帧级别的背景更新算法研究及实现验证

Background Updating Based on Pixel Level and Frame Level Algorithm and Implementation Verification
下载PDF
导出
摘要 讨论在复杂动态环境中基于象素级别和帧级别的背景更新算法及其实现验证.该算法可以对多种复杂的背景进行累积更新,通过实验验证表明该算法具有计算量小、运行速度快等优点;同时可以克服许多恶劣条件,包括虚影、摄像机的晃动和急剧的光线变化等.而且该算法可以用于实时监控跟踪领域,有效提高实时监控跟踪效率,也可以用于其他计算机视觉和视频分析应用领域. This article discusses the complex and dynamic environment based on the pixel level and frame level background updating algorithm and its implementation verification. The algorithm can be a variety of complex back- ground cumulative update. Through experimental verification, it shows that the algorithm has less computation, running speed, etco and can overcome many adverse conditions, including virtual shadow, camera shake and dras- tic changes in light. The algorithm can be used for real - time monitoring to track field, track effectively to improve the efficiency of real - time monitoring. And it can also be used for other computer vision and video analysis applications.
作者 郑芹
出处 《嘉应学院学报》 2011年第11期26-31,共6页 Journal of Jiaying University
关键词 背景更新 象素级别 帧级别 运动检测 background update pixel level frame level motion detection
  • 相关文献

参考文献4

二级参考文献12

  • 1彭仁明,贺春林.基于视频的车流量检测[J].西华师范大学学报(自然科学版),2004,25(4):404-408. 被引量:10
  • 2关向荣,任金昌.视频监视中背景的提取与更新算法[J].微电子学与计算机,2005,22(1):95-97. 被引量:13
  • 3张懿慧,徐晓夏,陈泉林.基于阴影抑制和自适应背景更新的车辆检测系统[J].上海大学学报(自然科学版),2005,11(5):465-471. 被引量:11
  • 4ISO/IEC.JTC1/SC29 WG11 N3093,MPEG-4 video verification model[S].
  • 5ISO/IEC.JTC1/SC29/WG11 N6828,MPEG-7 Overview[S].
  • 6Mo Xiaoran,Wilson R.Video modelling and segmentation using Gaussian mixture models[A].IEEE International Conference on Pattern Recognition[C].Cambridge,UK:Institute of Electrical and Electronics Engineers Inc,2004.854-857.
  • 7Blekas K,Likas A,Galatsanos N P,et al.A spatially constrained mixture model for image segmentation[J].IEEE Trans on Neural Networks,2005,16(2):494-498.
  • 8Greenspan H J,Goldberger M A.A probabilistic framework for spatio temporal video representation and indexing[A].Proceedings of the 7th European Conference on Computer Vision[C].New York:Springer,2002.461-475.
  • 9Greenspan H J,Goldberger M A.Probabilistic spacetime video modeling via piecewise GMM[J].IEEE Trans on Pattern Analysis and Machine Intelligence,2004,26(3):384-396.
  • 10Dempster A,Laird N,Rubin D.Maximum likelihood from incomplete data via the EM algorithm[J].Journal of the Royal Statistical Society:Series B,1977,39(1):1-38.

共引文献10

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部