Bayesian foreground and shadow detection in uncertain frame rate surveillance videos
In in this paper, we propose a new model regarding foreground and shadow detection in video sequences. The model works without detailed a priori object-shape information, and it is also appropriate for low and unstable frame rate video sources. Contribution is presented in three key issues: 1) we pr...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 17(2008), 4 vom: 01. Apr., Seite 608-21 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2008
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't |
| Zusammenfassung: | In in this paper, we propose a new model regarding foreground and shadow detection in video sequences. The model works without detailed a priori object-shape information, and it is also appropriate for low and unstable frame rate video sources. Contribution is presented in three key issues: 1) we propose a novel adaptive shadow model, and show the improvements versus previous approaches in scenes with difficult lighting and coloring effects; 2) we give a novel description for the foreground based on spatial statistics of the neighboring pixel values, which enhances the detection of background or shadow-colored object parts; 3) we show how microstructure analysis can be used in the proposed framework as additional feature components improving the results. Finally, a Markov random field model is used to enhance the accuracy of the separation. We validate our method on outdoor and indoor sequences including real surveillance videos and well-known benchmark test sets |
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| Beschreibung: | Date Completed 06.05.2008 Date Revised 08.04.2008 published: Print Citation Status MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2008.916989 |