Full metadata record
DC pole | Hodnota | Jazyk |
---|---|---|
dc.contributor.author | Arora, Noopur | |
dc.contributor.author | Shukla, Parul | |
dc.contributor.author | Biswas, Kanad K. | |
dc.contributor.editor | Skala, Václav | |
dc.date.accessioned | 2018-05-18T12:17:49Z | - |
dc.date.available | 2018-05-18T12:17:49Z | - |
dc.date.issued | 2016 | |
dc.identifier.citation | WSCG '2016: short communications proceedings: The 24th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech RepublicMay 30 - June 3 2016, p. 245-252. | en |
dc.identifier.isbn | 978-80-86943-58-9 | |
dc.identifier.issn | 2464-4617 | |
dc.identifier.uri | wscg.zcu.cz/WSCG2016/!!_CSRN-2602.pdf | |
dc.identifier.uri | http://hdl.handle.net/11025/29710 | |
dc.description.abstract | In this paper, we propose an approach for human activity recognition using gradient orientation of depth maps and spatio-temporal features from body-joints data. Our approach is based on an amalgamation of key local and global feature descriptors such as spatial pose, temporal variation in ‘joints’ position and spatio-temporal gradient orientation of depth maps. Additionally, we obtain a motion-induced global shape feature describing the motion dynamics during an action. Feature selection is carried out to select a relevant subset of features for action recognition. The resultant features are evaluated using SVM classifier. We validate our proposed method on our own dataset consisting of 11 classes and a total of 287 videos. We also compare the effectiveness of our method on the MSR-Action3D dataset. | en |
dc.format | 8 s. | cs |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.publisher | Václav Skala - UNION Agency | en |
dc.relation.ispartofseries | WSCG '2016: short communications proceedings | en |
dc.rights | © Václav Skala - UNION Agency | cs |
dc.subject | rozpoznávání akce | cs |
dc.subject | hluboké HOG | cs |
dc.subject | kynetika | cs |
dc.subject | data o těle a spojích | cs |
dc.title | Integrating depth-HOG and spatio-temporal joints data for action recognition | en |
dc.type | konferenční příspěvek | cs |
dc.type | conferenceObject | en |
dc.rights.access | openAccess | en |
dc.type.version | publishedVersion | en |
dc.subject.translated | action recognition | en |
dc.subject.translated | depth-HOG | en |
dc.subject.translated | kinectics | en |
dc.subject.translated | body-joints data | en |
dc.type.status | Peer-reviewed | en |
Vyskytuje se v kolekcích: | WSCG '2016: Short Papers Proceedings |
Soubory připojené k záznamu:
Soubor | Popis | Velikost | Formát | |
---|---|---|---|---|
Arora.pdf | Plný text | 604,7 kB | Adobe PDF | Zobrazit/otevřít |
Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam:
http://hdl.handle.net/11025/29710
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