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Article: Recognition of dynamic video contents with global probabilistic models of visual motion

TitleRecognition of dynamic video contents with global probabilistic models of visual motion
Authors
KeywordsMotion learning
Motion recognition
Probabilistic models
Video analysis
Issue Date2006
PublisherIEEE
Citation
Ieee Transactions On Image Processing, 2006, v. 15 n. 11, p. 3417-3430 How to Cite?
AbstractThe 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. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/132617
ISSN
2015 Impact Factor: 3.735
2015 SCImago Journal Rankings: 2.727
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorPiriou, Gen_HK
dc.contributor.authorBouthemy, Pen_HK
dc.contributor.authorYao, JFen_HK
dc.date.accessioned2011-03-28T09:27:02Z-
dc.date.available2011-03-28T09:27:02Z-
dc.date.issued2006en_HK
dc.identifier.citationIeee Transactions On Image Processing, 2006, v. 15 n. 11, p. 3417-3430en_HK
dc.identifier.issn1057-7149en_HK
dc.identifier.urihttp://hdl.handle.net/10722/132617-
dc.description.abstractThe 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. © 2006 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEEen_US
dc.relation.ispartofIEEE Transactions on Image Processingen_HK
dc.subjectMotion learningen_HK
dc.subjectMotion recognitionen_HK
dc.subjectProbabilistic modelsen_HK
dc.subjectVideo analysisen_HK
dc.titleRecognition of dynamic video contents with global probabilistic models of visual motionen_HK
dc.typeArticleen_HK
dc.identifier.emailYao, JF: jeffyao@hku.hken_HK
dc.identifier.authorityYao, JF=rp01473en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/TIP.2006.881963en_HK
dc.identifier.pmid17076401-
dc.identifier.scopuseid_2-s2.0-33750311690en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33750311690&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume15en_HK
dc.identifier.issue11en_HK
dc.identifier.spage3417en_HK
dc.identifier.epage3430en_HK
dc.identifier.isiWOS:000241391100015-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridPiriou, G=22433503700en_HK
dc.identifier.scopusauthoridBouthemy, P=7005146506en_HK
dc.identifier.scopusauthoridYao, JF=7403503451en_HK

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