Adaptive background modeling from an image sequence by using K-Means clustering

Abstract

Background subtraction is an essential technique in vision systems including foreground segmentation, object tracking and video surveillance system. Mixture of Gaussian (MOG) is a popular method for modeling adaptive background in many researches. However, the clustering technique and the number of clusters are different depending on their applications. In this paper, we proposed a novel method for constructing adaptive background from image sequences by using the Gaussian Mixture Model and K-Means clustering technique. Intensities of each pixel in the same coordinate from sequential image are collected. Distribution of intensity is analyzed by the Gaussian Mixture Model. Based on the intensity of background cluster and foreground cluster, the Gaussian distribution is divided into two clusters by K-Means clustering technique. The intensities in the cluster which has maximum member are averaged. The average intensity is used for background model. Nineteen image sequences were done in the experiments. The results show the feasibility of the proposed method.