Edge-segment-based Background Modeling: Non-parametric online background update

For background-subtraction-based moving object detection, reliable background modeling is the most important component. Pixel-based methods are sensitive to illumination change, and edge-based methods can solve illumination-related problems, but have shape distortion problems. In this paper, we propose an edge-segment-based statistical background modeling algorithm and an online update mechanism to detect moving objects from consecutive frames, which creates a balance between the pixel- and edge-based methods. Our background modeling method uses a statistical map to model the frequency of the background-edges, as distributions that comprise support regions approximated with a quadratic function and enhanced with color and gradient information, to overcome the edge-distortion problem by matching the edge-segments to the modeled distributions. To adjust the changing background in the scenes, we propose an online-background update step for every incoming frame that updates the statistical map and enhances the information held by the distributions support regions. Furthermore, our experiments show that the proposed method obtains better results and detects moving edges efficiently.