Calibrating a motion detection system by means of a distributed genetic algorithm

Motion detection systems for visual surveillance and monitoring purposes have aroused interest in the computer video community for many years. The main task of these applications is to identify (and track) moving targets. Usually, these applications requires that a large number of parameters is tuned in order to work properly. In the traffic monitoring application we have developed about thirty parameters concerning the detection algorithm have been considered as to be optimized. Genetic Algorithms (GAs) are an optimization technique which involves a search from a population of solutions rather than from a single point. Although they usually are very time-consuming, they owe a high intrinsic parallelism. Accordingly, this paper shows how a distributed implementation of a GA over a network of workstations can successfully accomplish the parameter optimization task within a motion detection system and achieve excellent performance within a reduced amount of time.