PTZ network configuration for optimal 3D coverage

During the last years, the need for security-oriented surveillance systems has grown higher and higher. Nowadays many public environments, such as airports, train stations, etc. are monitored by some sort of video-surveillance system in order to detect or prevent security issues. The involved technology ranges from the use of plain closed-circuit cameras (CCTV) to sophisticated computer-based video processing systems. The CCTV approach has been the only feasible choice in the past, and it is still widely used, however its limits are more and more evident: the increase of the number of sensors (modern surveillance systems can use hundreds of cameras) is often not matched by an adequate number of human operators, whose attention is spread on many different tasks and quickly decreases over time. Modern computer-based systems try to face these problems using automatic video analysis and understanding techniques, in order to cover wide areas and simultaneously highlight only the potential security issues and thus requiring the attention of a human operator only in a limited number of cases (e.g. [6, 5]). The research in this field has been very active and produced many techniques for video analysis and interpretation, but many works are limited to the use of static cameras. Only recently the research community started focusing on more sophisticated sensors like Pan-Tilt-Zoom (PTZ) cameras, and the research on dynamic, active networks of PTZ cameras is still limited (for an example of some recent works in this field, see [1]). Many of these works focus on exploiting the dynamic features of a network of PTZ cameras to improve tracking performance [3, 4, 13, 10, 12], while relatively few works address the problem of optimizing the camera coverage of the monitored area according to specific criteria. Angella et al. [2] propose a method to maximize the area coverage by using a 3D model of the observed zone, but their work only aims at finding a good initial camera displacemen- - t, which cannot be dynamically modified according to the observed data. Mittal and Davis [8, 7] also consider the presence of dynamic occluding objects in order to evaluate the visibility of the scene. Piciarelli et al. [11] propose a method to automatically and dynamically reconfigure the camera orientations and zoom levels using an Expectation-Maximization-based approach.