The paper proposes a novel change-detection algorithm for automated videosurveillance applications. The algorithm is based on the idea of incorporating into the background model a set of simple low-level features capable of capturing effectively "structural" (i.e. robust with respect to illumination variations) information. Thanks to this approach, and unlike most conventional change-detection algorithms, the proposed algorithm is capable of handling correctly still and slow objects as well as of working properly throughout very long time spans. Moreover, the algorithm can naturally interact with the higher-level processing modules found in advanced video-based surveillance systems in order to allow for .exible and intelligent background maintenance.