Advanced image-processing tools for counting people in tourist site-monitoring applications

This work aims at demonstrating the usefulness of exploiting novel image-processing tools for moving-object detection and classification in the context of an actual application involving the remote monitoring of a tourist site. The application concerns outdoor people counting for tourist-flow estimation in a constrained environment. The technical problems to be solved are concerned with: (a) the design and implementation of low-complexity background updating and change detection algorithms able to adapt themselves to the time-varying illumination scene conditions, and (b) the integration of real-time pattern-recognition tools in order to distinguish group of persons to be counted from other objects present in the scene. The achieved results have proven that the proposed system makes it possible to obtain reliable people counting in different environmental situations, with an absolute mean error at most equal to 10%. © 2001 Elsevier Science B.V.