Evaluation of an Adaptive Composite Gaussian Model in Video Surveillance

Abstract

Video surveillance systems seek to automatically identify events of interest in a variety of situations. Extracting a moving object from background is the most important step of the whole system. There are many approaches to track moving objects in a video surveillance system. These can be classified into three main groups: feature-based tracking, background subtraction, and optical flow techniques. Background subtraction is a region-based approach where the objective is to identify parts of the image plane that are significantly different to the background. In order to avoid the most common problems introduced by gradual illumination changes, waving trees, shadows, etc., the background scene requires a composite model. A mixture of Gaussian distributions is most popular. In this paper, we classify and discuss several recently proposed composite models. We have chosen one of these for implementation and evaluate its performance. We also analyzed its benefits and drawbacks, and designed an improved version of this model based on our experimental evaluation. One stationary camera has been used.