Dynamic hand gesture recognition from Bag-of-Features and local part model
This paper discusses the use of Bag-of-Features and a local part model approach for bare hand dynamic hand gesture recognition from video. We used dense sampling to extract local 3D multiscale whole-part features. We adopted three dimensional histograms of a gradient orientation (3D HOG) descriptor...
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| creator | Abid, M. R. Feng Shi Petriu, E. M. |
| description | This paper discusses the use of Bag-of-Features and a local part model approach for bare hand dynamic hand gesture recognition from video. We used dense sampling to extract local 3D multiscale whole-part features. We adopted three dimensional histograms of a gradient orientation (3D HOG) descriptor to represent features. K-means++ method has applied to cluster the visual words. Dynamic hand gesture classification was completed by using a Bag-of-features (BOF) and non-linear support vector machine (SVM) method. A BOF do not track the order of events. To counter the unordered events of BOF approach, we used a multiscale local part model to preserve temporal context. Initial experimental results on newly collected complex dataset show a higher level of recognition. |
| doi_str_mv | 10.1109/HAVE.2012.6374443 |
| format | Conference Proceeding |
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R. ; Feng Shi ; Petriu, E. M.</creator><creatorcontrib>Abid, M. R. ; Feng Shi ; Petriu, E. M.</creatorcontrib><description>This paper discusses the use of Bag-of-Features and a local part model approach for bare hand dynamic hand gesture recognition from video. We used dense sampling to extract local 3D multiscale whole-part features. We adopted three dimensional histograms of a gradient orientation (3D HOG) descriptor to represent features. K-means++ method has applied to cluster the visual words. Dynamic hand gesture classification was completed by using a Bag-of-features (BOF) and non-linear support vector machine (SVM) method. A BOF do not track the order of events. To counter the unordered events of BOF approach, we used a multiscale local part model to preserve temporal context. 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To counter the unordered events of BOF approach, we used a multiscale local part model to preserve temporal context. Initial experimental results on newly collected complex dataset show a higher level of recognition.</description><subject>3D HOG descriptor</subject><subject>bag-of-feature (BOF)</subject><subject>Computational modeling</subject><subject>dynamic hand gesture</subject><subject>Feature extraction</subject><subject>Gesture recognition</subject><subject>Hidden Markov models</subject><subject>Histograms</subject><subject>local part model</subject><subject>Support vector machines</subject><subject>Visualization</subject><isbn>9781467315685</isbn><isbn>1467315680</isbn><isbn>9781467315678</isbn><isbn>1467315672</isbn><isbn>9781467315661</isbn><isbn>1467315664</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVUMlKA0EUbBFBifMB4qV_YOLrvecYY2KUgJfgNbzpZWyZJfSMh_y9CebiqShqoShCHhjMGYPqabP4XM05MD7XwkgpxRUpKmOZ1EYwpY29_setuiXFOH4DwCmuuZR35P3l2GOXHP3C3tMmjNNPDjQHNzR9mtLQ05iHjj5jUw6xXAc86yM9m9vBYUsPmCfaDT609-QmYjuG4oIzsluvdstNuf14fVsutmWqYCorC-o0RkREYS3zWin03jAuwaIVmgHWmqtgjRO185E5EcGD46yWPnoQM_L4V5tCCPtDTh3m4_5ygPgFUL5N_Q</recordid><startdate>201210</startdate><enddate>201210</enddate><creator>Abid, M. R.</creator><creator>Feng Shi</creator><creator>Petriu, E. M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201210</creationdate><title>Dynamic hand gesture recognition from Bag-of-Features and local part model</title><author>Abid, M. R. ; Feng Shi ; Petriu, E. M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-98053153faa3881d655add712408a83610ab625e87c3bcdf1c3f0d0c21b4dfd03</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>3D HOG descriptor</topic><topic>bag-of-feature (BOF)</topic><topic>Computational modeling</topic><topic>dynamic hand gesture</topic><topic>Feature extraction</topic><topic>Gesture recognition</topic><topic>Hidden Markov models</topic><topic>Histograms</topic><topic>local part model</topic><topic>Support vector machines</topic><topic>Visualization</topic><toplevel>online_resources</toplevel><creatorcontrib>Abid, M. R.</creatorcontrib><creatorcontrib>Feng Shi</creatorcontrib><creatorcontrib>Petriu, E. M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Abid, M. R.</au><au>Feng Shi</au><au>Petriu, E. M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Dynamic hand gesture recognition from Bag-of-Features and local part model</atitle><btitle>2012 IEEE International Workshop on Haptic Audio Visual Environments and Games (HAVE 2012) Proceedings</btitle><stitle>HAVE</stitle><date>2012-10</date><risdate>2012</risdate><spage>78</spage><epage>82</epage><pages>78-82</pages><isbn>9781467315685</isbn><isbn>1467315680</isbn><eisbn>9781467315678</eisbn><eisbn>1467315672</eisbn><eisbn>9781467315661</eisbn><eisbn>1467315664</eisbn><abstract>This paper discusses the use of Bag-of-Features and a local part model approach for bare hand dynamic hand gesture recognition from video. We used dense sampling to extract local 3D multiscale whole-part features. We adopted three dimensional histograms of a gradient orientation (3D HOG) descriptor to represent features. K-means++ method has applied to cluster the visual words. Dynamic hand gesture classification was completed by using a Bag-of-features (BOF) and non-linear support vector machine (SVM) method. A BOF do not track the order of events. To counter the unordered events of BOF approach, we used a multiscale local part model to preserve temporal context. Initial experimental results on newly collected complex dataset show a higher level of recognition.</abstract><pub>IEEE</pub><doi>10.1109/HAVE.2012.6374443</doi><tpages>5</tpages></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | 3D HOG descriptor bag-of-feature (BOF) Computational modeling dynamic hand gesture Feature extraction Gesture recognition Hidden Markov models Histograms local part model Support vector machines Visualization |
| title | Dynamic hand gesture recognition from Bag-of-Features and local part model |
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