Modern background subtraction techniques can handle gradual illumination changes but can easily be confused by rapid ones. We propose a technique that overcomes this limitation by relying on a statistical model, not of the pixel intensities, but of the illumination effects. Because they tend to affect whole areas of the image as opposed to individual pixels, low-dimensional models are appropriate for this purpose and make our method extremely robust to illumination changes, whether slow or fast. We will demonstrate its performance by comparing it to two representative implementations of state-of-the-art methods, and by showing its effectiveness for occlusion handling in a real-time Augmented Reality context.
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