Adding a rigid motion model to foreground detection: application to moving object detection in rivers

Object detection in a dynamic background is a challenging task in many computer vision applications. 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 information 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 obtained during the training period. When available, prior knowledge can be incorporated into the foreground detection process to improve robustness and to decrease false detection. We apply this framework to moving object detection in rivers, one of the situations in which classic background subtraction algorithms fail. Our experiments show that the incorporation of prior motion data into background subtraction improves object detection.