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Adaptive Background Estimation using Intensity Independent Features

The problem of subtracting the background in an image sequence is central to computer vision. The general idea is to model the values of each pixel as a random variable. This approach has proved to be efficient when treating slowly varying changes or changes that are fairly periodic. In this paper we propose a novel method for background and foreground estimation that efficiently handles changes that also occur with non periodicity and fast. Furthermore, the method makes only very mild assumptions about the scene making it able to operate in a wide variety of conditions. This is done by introducing a novel set of invariants that are independent to the over all intensity level in the images. By using these features instead of the raw pixel data we automatically obtain a background estimator that is insensitive to rapid changes in lighting conditions. Furthermore, the features can be computed very efficiently using the so called integral image. Inspired by the work in [17] we update the probability model over time to make it able to handle new objects entering the background, but here we work directly with the histogram which reduces the execution time considerably. Finally, we present test data that shows that the model works well in some outdoor scenes. In particular it is shown that it can handle difficult outdoor scenes with rapidly bypassing clouds.