Improved background subtraction method for detecting moving objects based on GMM

Abstract

The background subtraction (BS) method is one of the most commonly used components in the detection of moving objects in computer vision systems, in which moving objects in image sequences are detected by comparing the background model with the current frame. Traditional moving object detection algorithms have some problems, such as changes in dynamic background, background variation due to changes of illumination, and being more sensitive to noise and shadows. In this study, an improved BS method based on the Gaussian mixture model (GMM) is proposed for the challenge in object detection. Not only is the influence of changes of illumination, and noise and shadows reduced, but the dynamic changes of natural scenes can also be tackled by using this method. The contribution lies in the following aspects: (i) a Gaussian background modeling method with less running time is proposed in the background modeling stage. The background is reconstructed based on GMM of the mean images of image blocks, aiming to simplify the calculations so as to improve the speed of the corresponding operations. (ii) In the foreground detection stage, a wavelet‐based, denoising method with the semisoft threshold function is applied to denoise the object images of the foreground. (iii) In the background maintenance stage, an adaptive background maintenance algorithm is proposed to dynamically update the background. Experimental results show that the computational complexity is reduced, while the adaptability and performance are improved by using this method. It outperforms the traditional methods in terms of both efficiency and robustness. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.