We focus on the problem of foreground segmentation in an outdoor environment with a static TV camera. Our application context is the visual surveillance of archeological sites. In this context the main aim is to detect the presence of people and to recognize their gestures in order to identify illegal actions. We concentrate solely on the primary step of moving object detection. In particular, the system should be capable of recovering the true shape of moving objects in order to allow for a classifier to discriminate people from any other moving objects, such as cars and animals. Moreover, the system should not be sensitive to changes in lighting, weather, number of people, etc., and it is required to work autonomously for long periods of time. A main problem in analyzing real outdoor daylight scenes is to deal with shadows cast by moving objects such as vehicles or pedestrians. In this paper a new approach for cast shadow removing is proposed. Our idea is to detect shadow points as points that are static for a short time and are characterized by a photometric gain, with respect to the reference background image, that is lower than unity and that we estimate for each new image through an optimization approach.