A perfect estimation of a background image does not lead to a perfect background subtraction: Analysis of the upper bound on the performance

Abstract

The quest for the “best” background subtraction technique is ongoing. Despite that a considerable effort has been undertaken to develop flexible and efficient methods, some elementary questions are still unanswered. One of them is the existence of an intrinsic upper bound to the performance. In fact, data are affected by noise, and therefore it is illusory to believe that it is possible to achieve a perfect segmentation. This paper aims at exploring some intrinsic limitations of the principle of background subtraction. The purpose consists in studying the impact of several limiting factors separately. One of our conclusions is that even if an algorithm would be able to calculate a perfect background image, it is not sufficient to achieve a perfect segmentation with background subtraction, due to other intrinsic limitations.