Abstract: Background modeling is a critical element of detecting and tracking moving objects．A adaptive threshold background modeling algorithm based on Chebyshev Inequality was proposed to solve the problem about background modeling of more real-time requirements in real-time video surveillance system．Firstly,Chebyshev inequality and the adaptive threshold are used to calculate the probability of each pixel belonging to the foreground or background,so the pixels are classified as the foreground points,background points and suspicious points．Secondly,kernel density estimation method is used to estimate the probability of these suspicious points for further discrimination．Finally,the background image is extracted through the real-time background update algorithm．Experimental results show that Chebyshev inequalities can quickly distinguish the foreground and background points.Further kernel density estimation me-thod can reduce the background segmentation error,and the real-time background image results are satisfactory.This algorithm significantly improves the computing speed,is suitable for real-time video monitoring system.

Key words: Chebyshev inequality,Adaptive threshold,Kernel density estimation,Background update algorithm