Detection of moving objects is a fundamental task in video based surveillance and security applications. Many detection systems use background estimation methods to model the observed environment. In outdoor surveillance, moving backgrounds (waving trees, clutter) and illumination changes (weather changes, reflections, etc.) are the major challenges for background modelling and the development of a single model that fulfils all these requirements is usually not possible. In this paper we present a background estimation technique for motion detection in non-static backgrounds that overcomes this problem. We introduce an enhanced background estimation architecture with a long-term model and a short-term model. Our system showed that fusion of the detections of these two complementary approaches, improves the quality and reliability of the detection results.