Robust object detection using a radial reach filter (RRF)

In this paper the authors report on a new algorithm used to separate an object from its background using a background image. In the past, simple background subtraction has been used because of its low processing costs and ease of implementation. However, because this method depends solely on brightness patterns in the object and shadows, it has problems such as an inability to deal with poor lighting conditions and an inability to detect regions in which the brightness levels of the object and shadows are similar. In order to resolve these problems, in this paper the authors propose a new filter process called a Radial Reach Filter (RRF). The authors define a new statistic called a Radial Reach Correlation (RRC) used to determine on a pixel-by-pixel basis the similar and dissimilar areas between a background image and a current scene. They then evaluate the local texture at pixel-level resolution while reducing the effects of variations in lighting. In addition, by introducing a mechanism to adjust the defined region adaptively based on local characteristics of the background image, the authors are able to work with various shadows and objects in the scene. The authors perform a theoretical evaluation and experiments using real images to demonstrate the validity of their proposed method. © 2004 Wiley Periodicals, Inc.