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.
|Number of pages||4|
|Journal||Proceedings - International Conference on Image Processing, ICIP|
|Publication status||Published - 2004|
|Event||2004 International Conference on Image Processing, ICIP 2004 - , Singapore|
Duration: 18 Oct 2004 → 21 Oct 2004