A Novel Method of Foreground Object Detection in Infrared Images

• Main Authors: Cheng Chung Chen, 陳證中
• Other Authors: Jen Hui Chuang
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/37852752466355775015

碩士 === 國立交通大學 === 多媒體工程研究所 === 96 === In this thesis, we propose a novel method of foreground object detection for infrared images. We generalize the Gaussian Mixture Model (GMM) to construct a new Regional Gaussian Mixture Model (RGMM), by adding two random variables of image coordinates. Since the models are built for the whole image, not for every image pixel, the number of RGMM is much smaller than that of GMM for common videos. After an initial background construction, the RGMMs are updated by examining the existence of previous RGMMs in a 5 5 neighborhood for each image pixel, followed by the identification of the best-fit model which is then used in the update process. Experimental results show that better separation of foreground object from background can be achieved by using RGMM for infrared images obtained by a camera with small movements.