The recent evolutions in embedded systems have now made the video sensor networks a
reality. A video sensor network consists of a large number of low cost camera-sensors
that are deployed in random manner. It pervades both the civilian and military fields with
huge number of applications in various areas like health-care, environmental monitoring,
surveillance and tracking. As most of the applications demand the knowledge of the
sensor-locations and the network topology before proceeding with their tasks, especially
those based on detecting events and reporting, the problem of localization and calibration
assumes a significance far greater than most others in video sensor network. The
literature is replete with many localization and calibration algorithms that basically rely
on some a-priori chosen nodes, called seeds, with known coordinates to help determine
the network topology. Some of these algorithms require additional hardware, like arrays
of antenna, while others require having to regularly reacquire synchronization among the
seedy so as to calculate the time difference of the received signals. Very few of these
localization algorithms use vision based technique.
In this work, a vision based technique is proposed for localizing and configuring
the camera nodes in video wireless sensor networks. The camera network is assumed
randomly deployed. One a-priori selected node chooses to act as the core of the network
and starts to locate some other two reference nodes. These three nodes, in turn,
participate in locating the entire network using tri-lateration method with some
appropriate vision characteristics. In this work, the vision characteristics that are used the
relationship between the height of the image in the image plane and the real distance
between the sensor node and the camera. Many experiments have been simulated to
demonstrate the feasibility of the proposed technique. Apart from this work, experiments
are also carried out to locate any other new object in the video sensor network.
The experimental results showcase the accuracy of building up one-plane
network topology in relative coordinate system and also the robustness of the technique
against the accumulated error in configuring the whole network