The large availability of different types of cameras and lenses, together with the reduction in price of video sensors, has contributed to a widespread use of video surveillance systems, which have become a widely adopted tool to enforce security and safety, in detecting and preventing crimes and dangerous events. The possibility for personalization of such systems is generally very high, letting the user customize the sensing infrastructure, and deploying ad-hoc solutions based on the current needs, by choosing the type and number of sensors, as well as by adjusting the different camera parameters, as field-of-view, resolution and in case of active PTZ cameras pan,tilt and zoom. Further there is also a possibility of event driven automatic realignment of camera network to better observe the occurring event. Given the above mentioned possibilities, there are two objectives of this doctoral study. First objective consists of proposal of a state of the art camera placement and static reconfiguration algorithm and secondly we present a distributive, co-operative and dynamic camera reconfiguration algorithm for a network of cameras. Camera placement and user driven reconfiguration algorithm is based realistic virtual modelling of a given environment using particle swarm optimization. A real time camera reconfiguration algorithm which relies on motion entropy metric extracted from the H.264 compressed stream acquired by the camera is also presented.