An Effective History-based Background Extraction System

Abstract

Problem statement: In many visions-based surveillance systems, the first step is accomplished by detecting moving objects resulted from subtraction of the current captured frame from the extracted background. So, the results of these systems mainly depend on the accuracy of the background image. Approach: In this study, a proposed background extraction system is presented to model the background using a simple method, to initialize the model, to extract the moving objects and to construct the final background. Our model saves the history of each pixel separately. It uses the saved information to extract the background using a probability-based method. It updates the history of the pixel consequently and according to the value of that pixel in the current captured image. Results: Results of the experiments certify that not only the quality of the final extracted background is the best between four recently re-implemented methods, but also the time consumption of the extraction is acceptable. Conclusion: Since History-based methods use temporal information extracted from the several previous frames, they are less sensitive to noise and sudden changes for extracting the background image.