Observation Functions in an Information Theoretic Approach for Scheduling Pan-Tilt-Zoom Cameras in Multi-target Tracking Applications

The vast streams of data created by camera networks render unfeasible browsing all data, relying only on human resources. Automation is required for detecting and tracking multiple targets by using multiple cooperating cameras. In order to effectively track multiple targets, autonomous active camera networks require adequate scheduling and control methodologies. Scheduling algorithms assign visual targets to cameras. Control methodologies set precise orientation and zoom references of the cameras. We take an approach based on information theory to solve the scheduling and control problems. Each observable target in the environment corresponds to a source of information for which an observation corresponds to a reduction of the uncertainty and, as such, a gain in the information. In this work we focus on the effect of observation functions within the information gain. Observation functions are shown to help avoiding extreme zoom levels while keeping smooth information gains.