Background subtraction by modeling pixel and neighborhood information

In applications of the computer vision field, a vision system is usually composed of several low level and high level components, stacked on top of each other. A better design of the lower level components usually results in better accuracy of higher level functions, such as object tracking, face recognition, and surveillance. In this paper, we focus on the low level component design, background construction, which is one of the most basic elements for a surveillance system. The proposed method eases the problems that usually occur in background construction, including aperture problem, vacillating background, and shadow removal. In conventional background construction methods, only the history information (vertical direction) of pixels is usually considered. In contrast, the proposed scheme not only uses the vertical direction but also the neighborhood information (horizontal direction). Experimental results show that the proposed scheme can detect objects more delicate, alleviate the aperture problem, and identify shadow and discard it from detected objects.