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] 作者單位： School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, ChinaHoneywell (China) Limited, Tianjin 300042, China 期 刊： 中南大學(xué)學(xué)報(bào)（英文版）   EISCI Journal： JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY(ENGLISH EDITION) 年，卷(期)： 2010, 17(1) 分類號(hào)： U4 Keywords： background modeling    background subtraction    Mart wavelet    binary discrete wavelet transform    shadow elimination    機(jī)標(biāo)分類號(hào)： TP3 O29 機(jī)標(biāo)關(guān)鍵詞： 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 基金項(xiàng)目： 國(guó)家自然科學(xué)基金，supported by Tianjin Subway Safety System, Honeywell Limited, China

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