A Comparative Study of Proposed Moving Object Detection Methods

Abstract

The detection of moving objects is a basic task for computer vision system. The performances of these systems are not sufficient for many applications. One of the main reasons is that the moving objet detection task has many difficulties in dealing with various constraints like the variations of the environment. A great number of methods were already proposed. We classify contributions reported in the literature in four approaches with a categorization based on inter-frame processing they adopt methods based on Inter-Frame Difference (IFD), those based on Background Modeling (BM), methods based on the Optical Flow (OF), and hybrid methods. In this paper, we present our proposed methods to detect moving objects. The first is a hybrid method that combines the inter-images difference based on entropy image and optical flow computed by a local method with a hierarchical coarse-to-fine optical flow estimation. The second is an adaptive background modeling based on dynamic matrix and spatio-temporal analyses of scenes. A comparative study by quantitative evaluations shows that the proposed BM method can detects foreground robustly and accurately from videos recorded by a static camera and which include several constraints such as sudden and gradual illumination changes, shaking camera, background component changes, ghost, and foreground speed.