Object detection in a dynamic background is a challenging task in many computer vision applica- tions. In some situations, the motion of objects can be predicted thanks to its regularity (e.g. vehicle motion, pedestrian motion). In this article, we propose to model such motion knowledge and to use it as additional infor- mation to help in foreground detection. The inclusion of object motion information provides a measure for distinguishing moving objects from a background that has similar sizes and brightness levels. This information is obtained by applying statistical methods on data ob- tained during the training period.When available, prior knowledge can be incorporated into the foreground de- tection process to improve robustness and to decrease false detection. We apply this framework to moving ob- ject detection in rivers, one of the situations in which classic background subtraction algorithms fail. Our ex- periments show that the incorporation of prior motion data into background subtraction improves object de- tection.