Maintenance of Blind Background Model for Robust Object Detection

Abstract

An adaptive background model plays an important role for object detection in a scene which includes illumination changes. An updating process of the background model is utilized to improve the robustness against illumination changes. However, the process sometimes causes a false-negative problem when a moving object stops in an observed scene. A paused object will be gradually trained as the background since the observed pixel value is directly used for the model update. In addition, the original background model hidden by the paused object cannot be updated. If the illumination changes behind the paused object, a false-positive problem will be caused when the object restarts to move. In this paper, we propose 1) a method to inhibit background training to avoid the false-negative problem, and 2) a method to update an original background region occluded by a paused object to avoid the false-positive problem. We have used a probabilistic approach and a predictive approach of the background model to solve these problems. The great contribution of this paper is that we can keep paused objects from being trained by modeling the original background hidden by them. And also, our approach has an ability to adapt to various illumination changes. Our experimental results show that the proposed method can detect stopped objects robustly, and in addition, it is also robust for illumination changes and as efficient as the state-of-the-art method.