Moving object detection zone using a block‐based background model - Omar Elharrouss; Abdelghafour Abbad; Driss Moujahid; Hamid Tairi (IET Computer Vision, 2018)
Background modelling is a critical case for background‐subtraction‐based approaches and also for a wide range of applications. The background generation becomes difficult when the scene is complex or an object stays for a long time in the scene. Here, the authors propose a block‐based background initialisation, using the sum of absolute difference (SAD), and modelling, using a block‐based entropy evaluation, with a low computational cost which making them feasible for embedded platform. In general, many background‐subtraction approaches are sensitive to sudden illumination change in the scene and cannot update the background image in scenes. The proposed background modelling approach analyses the illumination change problem. The moving object detection mask is developed using a threshold selected by computing the mean of the SAD between the blocks background and the blocks of the current frame. From the qualitative and quantitative results obtained by the authors approach compared with some existing methods, the authors approach is effective for background generation and moving objects detection.