This paper shows a method for extending efficient algorithms for scene understanding already developed and tested for fixed cameras to a mobile camera environment. Real-time change detection methods for mobile-head cameras are introduced. The architecture of the system can be divided in two phases. During the off-line phase the system creates a panoramic multi-layer background image using a small number of static background images. In the on-line phase the system compares the acquired images with a portion of the panoramic background. Different approaches to produce the change detection images are analyzed. Experimental results are presented in order to validate the proposed methods; their evaluation is performed by using receiving operator characteristic (ROC) curves. The Neyman-Pearson statistical criterion has been used for selecting of optimal change detection threshold. The presented results, in terms of probabilities of false and correct detection rates and real-time behavior, show that one of the studied methods can be used as the basis for higher level modules of an automatic video-surveillance system.