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Robust background subtraction in traffic video sequence
Abstract：For intelligent transportation surveillance, a novel background model based on Mart wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background model kept a sample of intensity values for each pixel in the image and used this sample to estimate the probability density function of the pixel intensity. The density function was estimated using a new Marr wavelet kernel density estimation technique. Since this approach was quite general, the model could approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame were transformed in the binary discrete wavelet domain, and background subtraction was performed in each sub-band. After obtaining the foreground, shadow was eliminated by an edge detection method. Experimental results show that the proposed method produces good results with much lower computational complexity and effectively extracts the moving objects with accuracy ratio higher than 90%, indicating that the proposed method is an effective algorithm for intelligent transportation system. 作者： Author： GAO Tao[1] LIU Zheng-guang[1] YUE Shi-hong[1] ZHANG Jun[1] MEI Jian-qiang[1] GAO Wen-chun[2] 作者单位： School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, ChinaHoneywell (China) Limited, Tianjin 300042, China 期 刊： 中南大学学报（英文版） EISCI Journal： JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY(ENGLISH EDITION) 年，卷(期)： 2010, 17(1) 分类号： U4 Keywords： background modeling background subtraction Mart wavelet binary discrete wavelet transform shadow elimination 机标分类号： TP3 O29 机标关键词： video background subtraction probability density function background model wavelet transforms kernel density estimation computational complexity results edge detection based effective approach objects domain quite novel image frame Marr new 基金项目： 国家自然科学基金，supported by Tianjin Subway Safety System, Honeywell Limited, China【Robust background subtraction in tra】相关文章：
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