Real-time motion region detection and contouring system

Many real world applications require real-time motion detection. Performance of a motion detection system should be fast enough so that moving objects in video can be detected and processed in real time. Once motion region in a video is detected, object tracking, image data mining, semantic meaning extraction, and other video/image processing techniques can be performed. There is a lot of research in real-time motion detection however most of these studies are either computationally expensive or not robust enough against real world image overheads like background clutter and pixel noise. In this paper, we propose a robust real-time motion detection system with low computational complexity. The proposed system finds the difference images from successive frames, applies filtering and the pixel clustering operation on them. Then, sub-contours of the observed motion regions are found using either minimum bounding coarse rectangles, minimum bounding rectangles using moments or minimum bounding convex hull methods. Finally, our contour merging algorithm merges sub-contours to identify moving object regions. This novel approach, detects moving objects in a video frame with tight enclosing boundary contours without sacrificing robustness. Since no constraint is attached to the technique, it is a good candidate to be used as motion detection of choice in wide range of applications. We obtained promising results in the experiments with real time data.