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Video object's behavior analyzing based on motion history image and hidden markov model

Video object's behavior analyzing based on motion history image and hidden markov model
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摘要 A novel method was proposed,which extracted video object' s track and analyzed video object's be-havior.Firstly,this method tracked the video object based on motion history image,and obtained the co-ordinate-based track sequence and orientation-based track sequence of the video object.Then the pro-posed hidden markov model(HMM)based algorithm was used to analyze the behavior of video object withthe track sequence as input.Experimental results on traffic object show that this method can achieve thestatistics of a mass of traffic objects'behavior efficiently,can acquire the reasonable velocity behaviorcurve of traffic object,and can recognize traffic object's various behaviors accurately.It provides a basefor further research on video obiect behavior. A novel method was proposed, which extracted video object' s track and analyzed video object' s be- havior. Firstly, this method tracked the video object based on motion history image, and obtained the co- ordinate-based track sequence and orientation-based track sequence of the video object. Then the pro- posed hidden markov model (HMM) based algorithm was used to analyze the behavior of video object with the track sequence as input. Experimental results on traffic object show that this method can achieve the statistics of a mass of traffic objects' behavior efficiently, can acquire the reasonable velocity behavior curve of traffic object, and can recognize traffic object' s various behaviors accurately. It provides a base for further research on video object behavior.
作者 孟繁锋
出处 《High Technology Letters》 EI CAS 2009年第3期319-324,共6页 高技术通讯(英文版)
基金 supported by the High Technology Research and Development Programme of China(No.2004AA742209)
关键词 隐马尔可夫模型 视频对象 行为 形象 历史 运动 基础 交通速度 motion history image, hidden markov model (HMM), track sequence, behavior analyzing
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  • 1V.N.Vapnik著 张学工译.统计学习理论的本质[M].清华大学出版社,2000.第2章.
  • 2[1]Durbin R,Eddy S,Krogh A,et al.生物序列分析蛋白质和核酸的概率论模型[M].北京:清华大学出版社,2002:14-77.
  • 3[2]Carrillo H,Lipman D.The Multiple Sequence Alignment Problem in Biology[J].SIAM Journal on Applied Mathematics,1988,48:1 073-1 082.
  • 4[3]Krogh A.Brown M,Milan I S,et al.Hidden Markov models Comp-utational Biology:Applications to Protein Modeling[J].Journal of Molecular Biology,1994,235:1 501-1 531.
  • 5[4]Gribskov M,McLachlan A D,Eisenberg D.Profile Analysis:Detection of Distantly Related Proteins[J].In Proceedings of the National Academy of science of the USA,volume 1987,84(4):355-4358.
  • 6[5]Gribskov M,Luthy R,Eisenberg D.Profile Analysis[J].Methods in Enzymology,1990,183:146-159.
  • 7[6]Krogh A.Two Methods for Improving Performance of HMM and Their Application for Gene Finding[J].In Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology(ISMB),1997:179-186.
  • 8[7]Hein J.Unified Approach to Alignment and Phylogenies[J].Methods in Enzymology,1990,183:626-645.
  • 9杨杨,计算机学报,1998年,21卷,增刊,297页
  • 10Weng J,IEEE Trans Pattern Anal Mach Intell,1992年,8期,806页

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