Robust detection of a set of outliers for image changes based on rerunning the regression

By comparing two images, which are captured with the same scene at different times, we can detect the image changes due to moving objects. To reduce the influence from the different intensity properties of the images, an intensity compensation scheme, which is based on the polynomial regression model, is employed. For an accurate detection of outliers alleviating the influence from a set of outliers, a simple technique that reruns the regression is employed. In this paper, the algorithm that iteratively reruns the regression is theoretically analyzed by observing the convergency of the estimates of the noise variance. Using an empirical compensation constant for the estimate is also proposed. The compensation enables the detection algorithm robust to the choice of thresholds for selecting outliers.