Optimizing Multi-Target Detection in Stochastic Environments with Active Smart Camera Networks

Active Smart Camera Networks (SCNs) have a wide range of applications in areas such as banks, airports and underground terminals, where it is necessary to monitor open plan spaces reliably and robustly. However, the monitoring efficiency in such environments is affected by various factors which are stochastic in nature, such as the target movement and arrival times, as well as the capabilities of the on-board camera detection module. This paper presents a modelling framework and a subsequent optimization-based solution towards the optimal reconfiguration of the camera network in order to minimize the expected number of undetected targets under uncertainties. The proposed solution is applicable to a variety of scenarios and does not require to have a full view of the area. Simulation results indicate that the proposed solution is robust to varying conditions and is able to achieve good monitoring performance with only a few cameras.