Informatics Publishing Limited

Jalal H. Awad ¹, Amir S. Almallah ¹

Affiliations
1 Department of Computer Science, College of Science, University of Mustansiriyah (UOM), IQ

Abstract

Moving object detection has been widely used in diverse discipline such as intelligent transportation systems, airport security systems, video monitoring systems, and so on. In this paper we proposed an edge segment based statistical background modeling algorithm, which can be implemented for moving edge detection in video surveillance system using static camera. The proposed method is an edge segment based, so it can help to exceed some of the difficulties that face traditional pixel based methods in updating background model or bringing out ghosts while a sudden change occurs in the background.As an edge segment based method it is robust to illumination variation and noise, it is also robust against the traditional difficulties that faces existing pixel based methods like the scattering of the moving edge pixels. Therefore they can’t utilize edge shape information. Some other edge segment based methods treat every edge segment equally creating edge mismatch due to non stationary background. The proposed method found elegant solution to this lake by using a model that uses the statistics of each background edge segment, so that it can model both the static and partially moving background edges using ordinary training images that may even contain moving objects.

Keywords

Background Modeling, Statistical Distribution Map, Moving Edge Segment, Edge Segment Matching.

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Amir S. Almallah ¹, Jalal H. Awad ¹

Affiliations
1 Department of Computer Science, College of Science, University of Mustansiriyah (UOM), IQ

Abstract

Automatic real time video monitoring and object detection is indeed a challenge since there are many criteria that should be taken In mind in designing and implementing algorithms for this sake. The criteria that should be considered for example are processing speed, scene illumination variation and dynamic outdoor environment. In this study we propose a fast, flexible and immune against illumination variation approach for moving object detection based on the combination of edge segment based background modeling and background subtraction techniques. The first technique is used for building robust and flexible statistical background model, while the other technique is used for the prime detection of moving object to be compared later with the flexible background. Thus this combination leads to computational reduction due to the second technique, and then flexible matching and precise detection due to the first technique.

Keywords

Background Subtraction, Movement Detection, Background Modeling, Statistical Distribution.

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