Research On Background Modeling Based On Non-Parametric Methods

Intelligent video surveillance system is widely application and development prospects, in this research field, the analysis on movement of image and analysis of background is an important research direction. The ultimate purpose of moving target detection in video and image sequences is extract motion area from the background image in image sequences to track and analyze moving targets,which become an basic on intelligent forecasting and visual processing. But because of the dynamic changes of the background image,we search an ideal background model is rarely exist. We pay more attention on how find a background model on complex scenes.Then we update real-time background to separate the prospects,at the same time we track the moving target, This is the focus of research, but also the main contents of this work. This paper mainly discuss from the following two aspects:(1)Paper introduces various conventional background modeling methods, analyzed and compared. And introduces basic theory of the background subtraction and typical background modeling method and detailed derivation, the nonlinear bayesian theory, kalman filtering theory and gaussian mixture model, detailed the gaussian mixture algorithm,at the same time I pointed out the faults of parameters density estimation. I introduced basic properties of density estimators and discussing the parameter estimation method and the density of histogram density estimation method and the kernel density estimation method.I analyzed the influence factors of the estimated and point out the density estimation under different conditions,then points out the advantages and disadvantages of histogram density estimation and analysis the kernel density estimation of the asymptotic convergence properties. Also I briefly introduced the other two density estimation method based on k-nearest neighbor and basis function expansion method.(2)We put forward a kind of stability of non-parameter estimation, secondly we discuss the shadow detection and inhibit error detection. Based on the above analysis and comparison,in our experiments, we will compare kernel density estimation method to gaussian mixture method, The most important purpose is demonstrating the effectiveness of the kernel density estimation algorithm. In tracking algorithm,I introduced theoretical knowledge of mean-shift algorithm and described all key variable selection principle and probability density estimation algorithm of mean-shift in the multi-dimensional space. We described mean-shift algorithm calculation steps on the basis of key variables chosen,then use a similarity functions and the bayes error estimation estimated samples accurately in order to get more accurate localization of target. Through the experiments prove the effectiveness of mean-shift algorithm.