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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW4/35S6KT8
Repositorysid.inpe.br/sibgrapi@80/2009/08.17.22.40
Last Update2009:08.17.22.40.22 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi@80/2009/08.17.22.40.23
Metadata Last Update2022:06.14.00.14.02 (UTC) administrator
DOI10.1109/SIBGRAPI.2009.29
Citation KeyGuoIshwKonr:2009:AcReVi
TitleAction recognition in video by covariance matching of silhouette tunnels
FormatPrinted, On-line.
Year2009
Access Date2025, May 18
Number of Files1
Size207 KiB
2. Context
Author1 Guo, Kai
2 Ishwar, Prakash
3 Konrad, Janusz
Affiliation1 Boston University
2 Boston University
3 Boston University
EditorNonato, Luis Gustavo
Scharcanski, Jacob
e-Mail Addresspi@bu.edu
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 22 (SIBGRAPI)
Conference LocationRio de Janeiro, RJ, Brazil
Date11-14 Oct. 2009
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2010-08-28 20:03:26 :: pi@bu.edu -> administrator ::
2022-06-14 00:14:02 :: administrator -> :: 2009
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsvideo analysis
action recognition
silhouette tunnel
covariance matching
generalized eigenvalues
AbstractAction recognition is a challenging problem in video analytics due to event complexity, variations in imaging conditions, and intra- and inter-individual action-variability. Central to these challenges is the way one models actions in video, i.e., action representation. In this paper, an action is viewed as a temporal sequence of local shape-deformations of centroid-centered object silhouettes, i.e., the shape of the centroid-centered object silhouette tunnel. Each action is represented by the empirical covariance matrix of a set of 13-dimensional normalized geometric feature vectors that capture the shape of the silhouette tunnel. The similarity of two actions is measured in terms of a Riemannian metric between their covariance matrices. The silhouette tunnel of a test video is broken into short overlapping segments and each segment is classified using a dictionary of labeled action covariance matrices and the nearest neighbor rule. On a database of 90 short video sequences this attains a correct classification rate of 97%, which is very close to the state-of-the-art, at almost 5-fold reduced computational cost. Majority-vote fusion of segment decisions achieves 100% classification rate.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2009 > Action recognition in...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW4/35S6KT8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW4/35S6KT8
Languageen
Target FileIEEE-PID949748_final_submission.pdf
User Grouppi@bu.edu
Visibilityshown
5. Allied materials
Next Higher Units8JMKD3MGPEW34M/46SJQ2S
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.14.19.43 16
sid.inpe.br/sibgrapi/2022/06.10.21.49 5
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group isbn issn label lineage mark mirrorrepository nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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