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Motion Detection in Real-Time Video Streams Using Moved Frame Background | ||
| Journal of University of Babylon | ||
| Article 1, Volume 18, Issue 1, 2011, Pages 386-395 | ||
| Authors | ||
| Amina A. Dawood; Balasim A. Hussean | ||
| Abstract | ||
| There are many approaches for motion detection in a real-time video stream. All of them are based on comparing of the current video frame with one from the previous frames or with background. Another approach is to compare the current frame not with the previous one but with the first frame in the video sequence. So, if there were no objects in the initial frame, comparison of the current frame with the first one will give us the whole moving object independently of its motion speed. But, the approach has a big disadvantage - what will happen, if there was, for example, a person on the first frame, but then he is gone? Yes, we'll always have motion detected on the place, where the person was. Of course, we can renew the initial frame sometimes, but still it will not give us good results in the cases where we can not guarantee that the first frame will contain only static background. But, there can be an inverse situation. The most efficient algorithms are based on building the so called background of the scene and comparing each current frame with the background. There are many approaches to build the scene, but most of them are too complex. Our approach for building the background is to get the first frame of the video stream as the background frame. And then we'll always compare the current frame with the background one. Our approach is to "move" the background frame to the current frame on the specified amount (we used 1 level per frame). We move the background frame slightly in the direction of the current frame - we are changing colors of pixels in the background frame by one level per frame. To build the background we use the Morph filter, because the implementation of this filter is more efficient, so the filter produce better performance. The idea of the filter is to preserve specified percentage of the source filter and to add missing percentage from overlay image | ||
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