A Background Reconstruction Algorithm Based on Modified Basic Sequential Clustering

Abstract

Based on the assumption that background appears with large appearance frequency, a new background reconstruction algorithm based on modified basic sequential clustering is proposed in this paper. First, pixel intensity in period of time are classified based on modified basic sequential clustering. Second, merging procedure is run to classified classes. Finally, pixel intensity classes, whose appearance frequencies are higher than a threshold, are selected as the background pixel intensity value, so the background model can represent the scene well. Compared with the background reconstruction method based on basic sequential clustering, the simulation results show that an assignment for the data is reached after the final cluster formation, at the same time those near classes are avoided at all and the effect of input order of data has been reduced greatly. And the background model can represent the scene well.