An Effective Foreground Detection Approach Using a Block-Based Background Modeling

The moving objects detection is considered as an important factor for many video surveillance applications. To assure a best detection a background model should be generated. This paper proposes a background modeling approach. To generate this model, we use both pixel-based and block-based processes to classify background pixels from those belong to the foreground. After that, to minimize the noise in the results of the background subtraction the structure-texture decomposition is applied on the absolute difference image. Just the structure component which contains the homogeneous parts of the image is used in the segmentation. The binary motion detection mask computation is made using a selected threshold. The experimental results demonstrate that our approach is effective and accurate for moving objects detection.