Moving Object Segmentation in Video Sequences using Vector Quantization

Abstract

Moving object segmentation is crucial in many computer vision applications such as video surveillance, automated inspection, and many others. The goal of moving object segmentation is to classify pixels as foreground or background; the foreground pixels forming the moving objects. A good segmentation method should be able to do segmentation when the scene is complex as well as adaptable to changes in the environment. Many methods have been proposed for segmentation; statistical methods are the most popular ones. These methods model the background based on statistical information extracted from incoming frames. In this study, we estimate the background with the concept of vector quantization. The motion mask is created by subtracting incoming frames from estimated background under various conditions especially when the color variation between background and foreground objects is high. We measure the performance by some metrics such as similarity and error-rate. The results have shown better accuracy of our proposed method and preserving the high quality background during the segmentation process. Keywords— Background Subtraction; Vector Quantization; Moving Object Segmentation; Motion Detection; Video Surveillance; Tracking;