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Change detection using a statistical model of the noise in color images

We present a novel change detection method using a statistical model of the image noise. Most change detection methods are based on gray-level images. However, color images can provide much richer scene information. One major problem to use the color images in change detection is how to combine three components in color space as a detection cue. We use the Euclidean color distance of three channels to measure the difference between two consecutive images. Specifically, we present a new noise model for each color channel. Through this modeling we can estimate the distribution of the Euclidean color distance for unchanged regions. We can find the optimal threshold to detect changes using this estimated distribution. Although we use the optimal threshold, inevitably there may be false classifications. To reject these erroneous cases, we adopt the graph cuts method that efficiently minimizes the global energy, which takes into account the effect of neighboring pixels.