In the framework of networked control systems, we focus on networks of autonomous
PTZ cameras. A large set of cameras communicating each other through a network
is a widely used architecture in application areas like video surveillance, tracking and motion.
First, we consider relative localization in sensor networks, and we tackle the issue of
investigating the error propagation, in terms of the mean error on each component of the
optimal estimator of the position vector. The relative error is computed as a function of the
eigenvalues of the network: using this formula and focusing on an exemplary class of networks
(the Abelian Cayley networks), we study the role of the network topology and the dimension
of the networks in the error characterization. Second, in a network of cameras one of the
most crucial problems is calibration. For each camera this consists in understanding what is
its position and orientation with respect to a global common reference frame. Well-known
methods in computer vision permit to obtain relative positions and orientations of pairs
of cameras whose sensing regions overlap. The aim is to propose an algorithm that, from
these noisy input data makes the cameras complete the calibration task autonomously, in a
distributed fashion. We focus on the planar case, formulating an optimization problem over
the manifold SO(2). We propose synchronous deterministic and distributed algorithms that
calibrate planar networks exploiting the cycle structure of the underlying communication
graph. Performance analysis and numerical experiments are shown. Third, we propose a
gossip-like randomized calibration algorithm, whose probabilistic convergence and numerical
studies are provided. Forth and finally, we design surveillance trajectories for a network of
calibrated autonomous cameras to detect intruders in an environment, through a continuous
graph partitioning problem
