Recognition of dynamic video contents with global probabilistic models of visual motion
The exploitation of video data requires methods able to extract high-level information from the images. Video summarization, video retrieval, or video surveillance are examples of applications. In this paper, we tackle the challenging problem of recognizing dynamic video contents from low-level moti...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 15(2006), 11 vom: 14. Nov., Seite 3417-30 |
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| Weitere Verfasser: | , |
| Format: | Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2006
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Evaluation Study Journal Article Research Support, Non-U.S. Gov't |
| Zusammenfassung: | The exploitation of video data requires methods able to extract high-level information from the images. Video summarization, video retrieval, or video surveillance are examples of applications. In this paper, we tackle the challenging problem of recognizing dynamic video contents from low-level motion features. We adopt a statistical approach involving modeling, (supervised) learning, and classification issues. Because of the diversity of video content (even for a given class of events), we have to design appropriate models of visual motion and learn them from videos. We have defined original parsimonious global probabilistic motion models, both for the dominant image motion (assumed to be due to the camera motion) and the residual image motion (related to scene motion). Motion measurements include affine motion models to capture the camera motion and low-level local motion features to account for scene motion. Motion learning and recognition are solved using maximum likelihood criteria. To validate the interest of the proposed motion modeling and recognition framework, we report dynamic content recognition results on sports videos |
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| Beschreibung: | Date Completed 28.11.2006 Date Revised 10.12.2019 published: Print Citation Status MEDLINE |
| ISSN: | 1941-0042 |