A Multiple Layer Background Model for Foreground Detection
Cheng Yong-mei · 2008
Foreground detection is an important research problem in visual surveillance.In this paper,we present a novel multiple layer background model to detect and classify foreground into three classes,moving object,stationary object and ghost.The background is divided into two layers,reference background and dynamic background.Single Gaussian model and Gaussian mixture model are used respectively.Compared with many existing background models,an unique characteristic of the proposed algorithm is that through analyzing the Gaussian distributions of the two layers,stationary object and ghost are correctly labeled.Real-time object detection and tracking system is developed and tested under indoor and outdoor scenes with various scenarios.Extensive experimental results demonstrate that the proposed algorithm is effective and efficient and the processing speed of the system reaches 15fps for the image size of 320×240.