Illumination independent change detection for real world image sequences

Abstract

Change detection plays a very important role in many vision applications. Most change detection algorithms assume that the illumination on a scene will remain constant. Unfortunately, this assumption is not necessarily valid outside a well-controlled laboratory setting. The accuracy of existing algorithms diminishes significantly when confronted with image sequences in which the illumination is allowed to vary. In this note, we present two techniques for change detection that have been developed to deal with the more general scenario where illuination is not assumed to be constant. A detailed description of both new methods, the derivative model method and the shading model method, is provided. Results are presented for applying each of the techniques discussed to various image pairs.