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 monitorin...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Teresa Ko, Stefano Soatto, Deborah Estrin
• Keywords: Background Subtraction Algorithms, Computer Vision, ECCV 2008, Fast Update Scheme, Foreground Objects, Slow Background Update, Temporal Variation