A Proposed Background Modeling Algorithm for Moving Object Detection Using Statistical Measures - Karim, Abdulamir A. (Iraqi Journal of Science, 2021)
Extracting moving object from video sequence is one of the most important stepsin the video-based analysis. Background subtraction is the most commonly usedmoving object detection methods in video, in which the extracted object will befeed to a higher-level process ( i.e. object localization, object tracking ).The main requirement of background subtraction method is to construct astationary background model and then to compare every new coming frame with itin order to detect the moving object.Relied on the supposition that the background occurs with the higher appearancefrequency, a proposed background reconstruction algorithm has been presentedbased on pixel intensity classification ( PIC ) approach. First, pixel intensity in apredetermined time period has been classified according to a proposed clusteringmethod, second, pixels frequency of those clusters has been calculated, finally, thecenter of the cluster with the higher pixel frequency has been chosen as thebackground pixel intensity value.The efficiency and effectiveness of the proposed algorithm has been confirmedthrough comparing its results with those of the most common traditional methods,besides , the results of the proposed algorithm in a number of testing environmentwhich are traffic monitoring and pedestrian surveillance shows that the proposedalgorithm can save space and economize computation time and give good accuracy.