Style of action based individual recognition in video sequences
We present a method for recognizing individuals from their ldquostyle of actionrdquo. Two forms of human recognition can be useful: the determination that an object is from the class of humans (which is called human detection), and the determination that an object is a particular individual from thi...
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| creator | Pratheepan, Y. Prasad, G. Condell, J.V. |
| description | We present a method for recognizing individuals from their ldquostyle of actionrdquo. Two forms of human recognition can be useful: the determination that an object is from the class of humans (which is called human detection), and the determination that an object is a particular individual from this class (this is called individual recognition). This paper focuses on the latter problem. A periodicity is detected in from a sequence of motion detected binary image frames by finding the maximum similarity measure between them. Based on the periodicity information the Motion History Image (MHI) is applied for each individual sequence to find out entire motion information of periodic action. The individual is then recognized using a partial Hausdorff Distance similarity measure and the SVM classification approach. |
| doi_str_mv | 10.1109/ICSMC.2008.4811452 |
| format | Conference Proceeding |
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Two forms of human recognition can be useful: the determination that an object is from the class of humans (which is called human detection), and the determination that an object is a particular individual from this class (this is called individual recognition). This paper focuses on the latter problem. A periodicity is detected in from a sequence of motion detected binary image frames by finding the maximum similarity measure between them. Based on the periodicity information the Motion History Image (MHI) is applied for each individual sequence to find out entire motion information of periodic action. The individual is then recognized using a partial Hausdorff Distance similarity measure and the SVM classification approach.</description><identifier>ISSN: 1062-922X</identifier><identifier>ISBN: 142442383X</identifier><identifier>ISBN: 9781424423835</identifier><identifier>EISSN: 2577-1655</identifier><identifier>EISBN: 1424423848</identifier><identifier>EISBN: 9781424423842</identifier><identifier>DOI: 10.1109/ICSMC.2008.4811452</identifier><identifier>LCCN: 2008903109</identifier><language>eng</language><publisher>IEEE</publisher><subject>Fingers ; Humans ; Intelligent robots ; Intelligent systems ; Iris ; Motion detection ; Security ; Support vector machine classification ; Support vector machines ; Video sequences</subject><ispartof>2008 IEEE International Conference on Systems, Man and Cybernetics, 2008, p.1237-1242</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4811452$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4811452$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Pratheepan, Y.</creatorcontrib><creatorcontrib>Prasad, G.</creatorcontrib><creatorcontrib>Condell, J.V.</creatorcontrib><title>Style of action based individual recognition in video sequences</title><title>2008 IEEE International Conference on Systems, Man and Cybernetics</title><addtitle>ICSMC</addtitle><description>We present a method for recognizing individuals from their ldquostyle of actionrdquo. Two forms of human recognition can be useful: the determination that an object is from the class of humans (which is called human detection), and the determination that an object is a particular individual from this class (this is called individual recognition). This paper focuses on the latter problem. A periodicity is detected in from a sequence of motion detected binary image frames by finding the maximum similarity measure between them. Based on the periodicity information the Motion History Image (MHI) is applied for each individual sequence to find out entire motion information of periodic action. The individual is then recognized using a partial Hausdorff Distance similarity measure and the SVM classification approach.</description><subject>Fingers</subject><subject>Humans</subject><subject>Intelligent robots</subject><subject>Intelligent systems</subject><subject>Iris</subject><subject>Motion detection</subject><subject>Security</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><subject>Video sequences</subject><issn>1062-922X</issn><issn>2577-1655</issn><isbn>142442383X</isbn><isbn>9781424423835</isbn><isbn>1424423848</isbn><isbn>9781424423842</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkM1KAzEUheNPwU71BXSTF5iae5M0yUpkqFqouGgX3ZVMciOROqOdVujbO2rB1TmcD77FYewaxBhAuNtZtXiuxiiEHSsLoDSesAIUKoXSKnvKhqiNKWGi9dk_kKtzNgQxwdIhrgas-BE4IXvjBSu67k0IFArskN0tdocN8TZxH3a5bXjtO4o8NzF_5bj3G76l0L42-Rfmhvcrtbyjzz01gbpLNkh-09HVMUds-TBdVk_l_OVxVt3Py-zErgxJI2GqZQrKxGBD9MrViA6BrAkJojRa6AhOW5OsDGT6nryIULvoQI7YzZ82E9H6Y5vf_fawPj4ivwE5X0-V</recordid><startdate>200810</startdate><enddate>200810</enddate><creator>Pratheepan, Y.</creator><creator>Prasad, G.</creator><creator>Condell, J.V.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200810</creationdate><title>Style of action based individual recognition in video sequences</title><author>Pratheepan, Y. ; Prasad, G. ; Condell, J.V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-cf52e2fb3fc47dc8cda49b22921e87cf1d37505d19587f83ce7d19fa0d1b9d913</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Fingers</topic><topic>Humans</topic><topic>Intelligent robots</topic><topic>Intelligent systems</topic><topic>Iris</topic><topic>Motion detection</topic><topic>Security</topic><topic>Support vector machine classification</topic><topic>Support vector machines</topic><topic>Video sequences</topic><toplevel>online_resources</toplevel><creatorcontrib>Pratheepan, Y.</creatorcontrib><creatorcontrib>Prasad, G.</creatorcontrib><creatorcontrib>Condell, J.V.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library Online</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Pratheepan, Y.</au><au>Prasad, G.</au><au>Condell, J.V.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Style of action based individual recognition in video sequences</atitle><btitle>2008 IEEE International Conference on Systems, Man and Cybernetics</btitle><stitle>ICSMC</stitle><date>2008-10</date><risdate>2008</risdate><spage>1237</spage><epage>1242</epage><pages>1237-1242</pages><issn>1062-922X</issn><eissn>2577-1655</eissn><isbn>142442383X</isbn><isbn>9781424423835</isbn><eisbn>1424423848</eisbn><eisbn>9781424423842</eisbn><abstract>We present a method for recognizing individuals from their ldquostyle of actionrdquo. Two forms of human recognition can be useful: the determination that an object is from the class of humans (which is called human detection), and the determination that an object is a particular individual from this class (this is called individual recognition). This paper focuses on the latter problem. A periodicity is detected in from a sequence of motion detected binary image frames by finding the maximum similarity measure between them. Based on the periodicity information the Motion History Image (MHI) is applied for each individual sequence to find out entire motion information of periodic action. The individual is then recognized using a partial Hausdorff Distance similarity measure and the SVM classification approach.</abstract><pub>IEEE</pub><doi>10.1109/ICSMC.2008.4811452</doi><tpages>6</tpages></addata></record> |
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| ispartof | 2008 IEEE International Conference on Systems, Man and Cybernetics, 2008, p.1237-1242 |
| issn | 1062-922X 2577-1655 |
| language | eng |
| recordid | cdi_ieee_primary_4811452 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Fingers Humans Intelligent robots Intelligent systems Iris Motion detection Security Support vector machine classification Support vector machines Video sequences |
| title | Style of action based individual recognition in video sequences |
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