GSM-MRF based classification approach for real-time moving object detection 被引量：1

GSM-MRF based classification approach for real-time moving object detection

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摘要 Statistical and contextual information are typically used to detect moving regions in image sequences for a fixed camera.In this paper,we propose a fast and stable linear discriminant approach based on Gaussian Single Model(GSM)and Markov Random Field(MRF).The performance of GSM is analyzed first,and then two main improvements corresponding to the drawbacks of GSM are proposed:the latest filtered data based update scheme of the background model and the linear classification judgment rule based on spatial-temporal feature specified by MRF.Experimental results show that the proposed method runs more rapidly and accurately when compared with other methods. Statistical and contextual information are typically used to detect moving regions in image sequences for a fixed camera. In this paper, we propose a fast and stable linear discriminant approach based on Gaussian Single Model （GSM） and Markov Random Field （MRF）, The performance of GSM is analyzed first, and then two main improvements corresponding to the drawbacks of GSM are proposed： the latest filtered data based update scheme of the background model and the linear classification judgment rule based on spatial-temporal feature specified by MRF. Experimental results show that the proposed method runs more rapidly and accurately when compared with other methods.

作者 Xiang PAN Yi-jun WU

机构地区 Institute of Information and Communication Engineering

出处《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2008年第2期250-255,共6页 浙江大学学报（英文版）A辑（应用物理与工程）

基金 Project (No. 10577017) supported by the National Natural Science Foundation of China

关键词 Moving object detection Markov Random Field （MRF） Gaussian Single Model （GSM） Fisher Linear Discriminant Analysis （FLDA） 图象识别技术移动对象检测辨别式分析计算机

分类号 TP391.41 [自动化与计算机技术—计算机应用技术]

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