Image-based systems are commonly used in different businesses and industries. These systems are composed of a video camera that obtains and records images within its sensory field. For example, a video camera provides a video record of whatever is within the field of view (FOV) of its lens. The obtained images may then be monitored by a human operator and/or reviewed later by a human operator. Recent progress has allowed such images to be monitored also by an automated system, improving performance and saving human labor.
Effective automatic analysis of the images requires knowledge of the video camera's intrinsic parameters, its geometry and relationship with other sensors, geographical location, and/or relationship of actual measurement of objects in the camera's field of view with other image counterparts. Current systems use error-prone manual sensor calibration processes to obtain such information, and are time consuming and labor intensive. In addition, maintaining calibration between sensors in a network is a daunting task, since a slight change in the position of a sensor requires the calibration process to be repeated. Further, the currently available auto-calibration techniques use image-based information solely and, hence, are limited in their application and reliability. In addition, these techniques are unable to perform calibration or geo-registration without manual intervention.