A SUPPORT VECTOR DATA DESCRIPTION APPROACH FOR BACKGROUND MODELING IN VIDEOS WITH QUASI-STATIONARY BACKGROUNDS

Abstract

Video segmentation is one of the most important tasks in high-level video processing applications. Stationary cameras are usually used in applications such as video surveillance and human activity recognition. However, possible changes in the background of the video such as waving flags, fluctuating monitors, water surfaces, etc. make the detection of objects of interest particularly challenging. These types of backgrounds are called quasi-stationary backgrounds. In this paper we propose a novel, non-statistical technique to generate a background model and use this model for background subtraction and foreground region detection in the presence of such challenges. The main advantage of the proposed method over the state of the art is that unlike statistical techniques the accuracy of foreground regions is not limited to the estimate of the probability density. Also, the memory requirements of our method are independent of the number of training samples. This makes the proposed method useful in various scenarios including the presence of slow changes in the background. A comprehensive study is presented on the efficiency of the proposed method. Its performance is compared with various existing techniques quantitatively and qualitatively to show its superiority in various applications.