A Calibration Algorithm for Multi-camera Visual Surveillance Systems Based on Single-View Metrology

The growing concerns about persons security and the increasing popularity of pan-tilt-zoom (PTZ) cameras, have been raising the interest on automated master-slave surveillance systems. Such systems are typically composed by (1) a fixed wide-angle camera that covers a large area, detects and tracks moving objects in the scene; and (2) a PTZ camera, that provides a close-up view of an object of interest. Previously published approaches attempted to establish 2D correspondences between the video streams of both cameras, which is a ill-posed formulation due to the absence of depth information. On the other side, 3D-based approaches are more accurate but require more than one fixed camera to estimate depth information. In this paper, we describe a novel method for easy and precise calibration of a master-slave surveillance system, composed by a single fixed wide-angle camera. Our method exploits single view metrology to infer 3D data of the tracked humans and to self-perform the transformation between camera views. Experimental results in both simulated and realistic scenes point for the effectiveness of the proposed model in comparison with the state-of-the-art.