In recent years there has been an ever-growing interest for the concept of Unmanned Aerial Vehicles, UAV´s, and large research efforts are invested in this area today. One major field of application for such systems equipped with electro-optical imaging systems is aerial surveillance and reconnaissance, and for this purpose more autonomous sensor systems equipped with robust and efficient target tracking algorithms are needed. In this thesis, such a tracking algorithm has been studied and improved by the incorporation of an efficient Kalman filter. The result is a robust tracking algorithm that has been implemented in efficient C++ code and made to work in real-time to perform image based sensor control on an experimental camera system platform developed in the SIREOS project at FOI. The thesis has also been concerned with the development of an algorithm for position estimation (geolocation) of stationary and moving ground targets. The position of a stationary target is estimated by iterative triangulation in a global reference system, based on sequential data from the tracking algorithm and the platform navigation system. For the purpose of tracking moving ground targets, a measure of the tracking quality of an image reference region has been developed as well. This measure will allow for an autonomous choice of reference regions to track and geolocate in image sequences. Such geolocated stationary regions generate a reference system that admits geolocation of moving ground targets.