Background Modelling Using Edge-Segment Distributions

We propose an edge‐segment‐based statistical background modelling algorithm to detect the moving edges for the detection of moving objects using a static camera. Traditional pixel intensity‐based background modelling algorithms face difficulties in dynamic environments since they cannot handle sudden changes in illumination. They also bring out ghosts when a sudden change occurs in the scene. To cope with this issue, intensity and noise robust edge‐based features have emerged. However, existing edge‐pixel‐based methods suffer from scattered moving edge pixels since they cannot utilize the shape. Moreover, traditional segment‐ based methods cannot handle edge shape variations and miss moving edges when they come close to the background edges. Unlike traditional approaches, our proposed method builds the background model from ordinary training frames that may contain moving objects. Furthermore, it does not leave any ghosts behind. Moreover, our method uses an automatic threshold for every background edge distribution for matching. This makes our approach robust to illumination change, camera movement and background motion. Experiments show that our method outperforms others and can detect moving edges efficiently despite the above mentioned difficulties