Background Subtraction on Distributions

Environmental monitoring applications present a challenge to current background subtraction algorithms that analyze the temporal variability of pixel intensities, due to the complex texture and motion of the scene. They also present a challenge to segmentation algorithms that compare intensity or color distributions between the foreground and the background in each image independently, because objects of interest such as animals have adapted to blend in. Therefore, we have developed a background modeling and subtraction scheme that analyzes the temporal variation of intensity or color distributions, instead of either looking at temporal variation of point statistics, or the spatial variation of region statistics in isolation. Distributional signatures are less sensitive to movements of the textured background, and at the same time they are more robust than individual pixel statistics in detecting foreground objects. They also enable slow background update, which is crucial in monitoring applications where processing power comes at a premium, and where foreground objects, when present, may move less than the background and therefore disappear into it when a fast update scheme is used. Our approach compares favorably with the state of the art both in generic low-level detection metrics, as well as in application-dependent criteria.