Hybrid background modeling for long-term and short-term illumination changes

Background modeling has been widely researched to detect moving objects from image sequences. It is necessary to adapt the background model various changes of illumination condition. Recent years, a hybrid type of background model which consists of more than one background model has been used for object detection since it is very adaptable to illumination changes. In this paper, we also propose a new hybrid type of background model named "Hybrid Spatial-Temporal Background Model". Our model consists of two different kinds of background models. One is pixel-level background model which adapts to long-term illumination changes. The other is spatial-temporal background model which adapts to short-term illumination changes. Our experimental results demonstrate superiority of our method to some related works.