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Target-color learning and its detection for non-stationary scenes by nearest neighbor classification in the spatio-color space

We propose a method for detecting foreground objects in non-stationary scenes. The method can (1) detect arbitrary foreground objects without any prior knowledge of them, (2) identify background pixels under various changes in a background scene, and (3) detect minor difference between the background and target colors. Online detection is realized by the nearest neighbor classifier in the 5D xy-YUV space (the spatio-color space), consisting of the x and y coordinates of an image and Y, U, and V colors, which holds rectified training data of background colors and automatically learned target colors. We conducted experiments to confirm the effectiveness of our method.