Mean-Shift Background Image Modelling

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of IEEE International Conference on Image Processing 2004, 2004, pp. 3399 - 3402
Issue Date:
2004-01
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Background modelling is widely used in computer vision for the detection of foreground objects in a frame sequence. The more accurate the background model, the more correct is the detection of the foreground objects. In this paper, we present an approach to background modelling based on a mean-shift procedure. The mean shift vector convergence properties enable the system to achieve reliable background modelling. In addition, histogram-based computation and the new concept of local basins of attraction allow us to meet the stringent real-time requirements of video processing.
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