Automatic Robust Background Modeling Using Multivariate Non-parametric Kernel Density Estimation for Visual Surveillance

Abstract

The final goal for many visual surveillance systems is automatic understanding of events in a site. Higher level processing on video data requires certain lower level vision tasks to be performed. One of these tasks is the segmentation of video data into regions that correspond to objects in the scene. Issues such as automation, noise robustness, adaptation, and accuracy of the model must be addressed. Current background modeling techniques use heuristics to build a representation of the background, while it would be desirable to obtain the background model automatically. In order to increase the accuracy of modeling it needs to adapt to different parts of the same scene and finally the model has to be robust to noise. The building block of the model representation used in this paper is multivariate non-parametric kernel density estimation which builds a statistical model for the background of the video scene based on the probability density function of its pixels. A post processing step is applied to the background model to achieve the spatial consistency of the foreground objects.