Background Subtraction Using Local SVD Binary Pattern

Lili Guo, Dan Xu, Zhenping Qiang
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
subtraction is a basic problem for change detection in videos and also the first step of high-level computer vision applications. Most background subtraction methods rely on color and texture feature. However, due to illuminations changes in different scenes and affections of noise pixels, those methods often resulted in high false positives in a complex environment. To solve this problem, we propose an adaptive background subtraction model which uses a novel Local SVD Binary Pattern (named
more » ... Pattern (named LSBP) feature instead of simply depending on color intensity. This feature can describe the potential structure of the local regions in a given image, thus, it can enhance the robustness to illumination variation, noise, and shadows. We use a sample consensus model which is well suited for our LS-BP feature. Experimental results on CDnet 2012 dataset demonstrate that our background subtraction method using LSBP feature is more effective than many state-of-the-art methods.
doi:10.1109/cvprw.2016.148 dblp:conf/cvpr/GuoXQ16 fatcat:r7cmnecrkrboneloaq25qemany