Abstract: We propose a novel method for combining information streamed by a multi-sensor system for visual surveillance. Information fusion occurs in two phases during which all cameras are calibrated with respect to independent global Cartesian reference frames (set on the ground plane) and then all frames are registered into a single coordinate system. The development of automatic calibration and registering of visual data is crucial in visual surveillance applications because it makes easier to install the monitoring infrastructure and, consequently, to develop more accessible Visual Surveillance tools for the public domain. Machine learning techniques are believed to offer the best mathematical tools to handle the uncertainty and incomplete nature of surveillance data.