Anomaly detection in non-stationary backgrounds

In this paper, several algorithms are considered as solutions for detecting anomalies in images which are inherently non-stationary, i.e., the images contain more than one type of background. We conclude that a recent algorithm suggested by A. Schaum is most successful when coupled with several variations which we suggest. In particular, in concurrence with Schaum, for pixels in transition zones between two neighboring stationary areas, it is crucial to choose or construct a covariance matrix which is appropriate for that particular area. Methods to choose both the sample covariance matrix and the estimated local mean will be discussed.