Background Subtraction Based on Fusion of Color and Local Patterns

Md Rifat Arefin, Farkhod Makhmudkhujaev, Oksam Chae, Jaemyun Kim

Abstract: Segmentation of foreground objects using background subtraction methods is popularly used in a wide variety of application areas such as surveillance, tracking, and human pose estimation. Many of the background subtraction methods construct a background model in a pixel-wise manner using color information that is sensitive to illumination variations. In the recent past, a number of local feature descriptors have been successfully applied to overcome such issues. However, these descriptors still suffer from over-sensitivity and sometimes unable to differentiate local structures. In order to tackle the aforementioned problems of existing descriptors, we propose a novel edge based descriptor, Local Top Directional Pattern (LTDP), that represents local structures in a pattern form with aid of compass masks providing information of top local directional variations. Moreover, to strengthen the robustness of the pixel-wise background model and get benefited from each other, we combine both color and LTDP features. We evaluate the performance of our method on the publicly available change detection datasets. The results of extensive experiments demonstrate the better performance of our method compared to other state-of-the-art unsupervised methods.