Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A multiscale co-linearity statistic based approach to robust background modeling
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
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The accurate detection of moving objects is an important step in the process of tracking and recognition in many real-time video surveillance applications. In this paper, we propose a combination of block-based detection and a pixel-based Gaussian Mixture Model (GMM) for moving object detection. Compared with traditional pixel-based algorithms which update all pixels for every frame, our algorithm has the ability to selectively update region informatin within each frame, while offering the capability to refine the silhouette of a foreground object. The algorithm offers an efficient trade-off between complexity and detection performance. The results show improved detection in the presence of high camera noise, high level compression artefacts, camera movements and dynamic background conditions.