Background Modelling Using Edge-Segment Distributions

Abstract

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

On the Role and the Importance of Features for Background Modeling and Foreground Detection

“…Edges based approaches used in addition with intensity or color features are the most investigated approaches and allow to combine the advantage of the two features. Forthe approahces based on edges alone, only two main works emerged that are the works of based on edge segments [365][412] [265][366] [264][411] [266] and the work based on subpixel edge [226]. These approaches appear to be relevant too and merit to be more investigated.…”

“…Thus, basic comparison of edge-segments produces similar results as edge-pixel-based approaches. To solve this problem, statistical edge-segment-based methods extract movement of edge-segments including edge distortion [365][412] [265][366] [264][411] [266]. Thus, these methods solve the edge-variation problem by accumulating edge existence from a training sequence [267].…”

Unattended object detection based on edge-segment distributions

“…This detection requires to isolate the detected objects without any foreground that may be present (such as persons or other moving objects) from the incoming frame. Our method is based on a foreground detection method [7], which creates edge-segment distributions from a training sequence as a background and incoming frames. The system adds color and gradient information to the background to disambiguate foreground edges that are confused with background.…”

“…Moreover, existing edge-based methods [6] have many false alarms, because they use a simple edge dif- ferencing method. To solve this problem, edge-segmentbased methods [7,8,10,11] model background using the connected edges instead.…”