Group Behavior Recognition for Gesture Analysis
This paper analyzes the movements of the human body limbs (hands, feet and head) and center of gravity in order to detect and analyze simple actions such as walking and running. We propose a novel vision of the human body, by considering the limbs as cooperative agents that form a hierarchy of coope...
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| Veröffentlicht in: | IEEE transactions on circuits and systems for video technology 2008-02, Vol.18 (2), p.211-222 |
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| creator | Kosta, G. Benoit, M. |
| description | This paper analyzes the movements of the human body limbs (hands, feet and head) and center of gravity in order to detect and analyze simple actions such as walking and running. We propose a novel vision of the human body, by considering the limbs as cooperative agents that form a hierarchy of cooperative teams: the whole body. The movements are analyzed at individual level and at team level using a modular hierarchical structure. Knowledge of the high-level team actions (such as ldquowalkingrdquo) improves the pertinence of our predictions on the low-level individual actions (foot is moving back and forth) and allows us to compensate for missing or noisy data produced by the feature extraction system. In terms of group behavior recognition, we propose a novel framework for online probabilistic plan recognition in cooperative multiagent systems: the Multiagent Hidden Markov mEmory Model (M-AHMEM), which is a dynamic Bayesian network. Experiments on an existing video database using different models of the human body show the feasibility of the approach. |
| doi_str_mv | 10.1109/TCSVT.2007.913968 |
| format | Article |
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We propose a novel vision of the human body, by considering the limbs as cooperative agents that form a hierarchy of cooperative teams: the whole body. The movements are analyzed at individual level and at team level using a modular hierarchical structure. Knowledge of the high-level team actions (such as ldquowalkingrdquo) improves the pertinence of our predictions on the low-level individual actions (foot is moving back and forth) and allows us to compensate for missing or noisy data produced by the feature extraction system. In terms of group behavior recognition, we propose a novel framework for online probabilistic plan recognition in cooperative multiagent systems: the Multiagent Hidden Markov mEmory Model (M-AHMEM), which is a dynamic Bayesian network. Experiments on an existing video database using different models of the human body show the feasibility of the approach.</description><identifier>ISSN: 1051-8215</identifier><identifier>EISSN: 1558-2205</identifier><identifier>DOI: 10.1109/TCSVT.2007.913968</identifier><identifier>CODEN: ITCTEM</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Applied sciences ; Bayesian methods ; Bayesian network ; Biological system modeling ; Detection, estimation, filtering, equalization, prediction ; dynamic ; Dynamical systems ; Dynamics ; Exact sciences and technology ; Feature extraction ; Foot ; Gravity ; group behavior ; Hidden Markov models ; human action ; Human body ; Humans ; Information, signal and communications theory ; Legged locomotion ; Limbs ; Mathematical models ; Miscellaneous ; Movements ; Multiagent systems ; particle filter ; plan recognition ; Rao-Blackwell ; Recognition ; Signal and communications theory ; Signal processing ; Signal, noise ; Spatial databases ; Studies ; Telecommunications and information theory</subject><ispartof>IEEE transactions on circuits and systems for video technology, 2008-02, Vol.18 (2), p.211-222</ispartof><rights>2008 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2008</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c385t-36f7b9cd47399eaba566b2ec5e477a3b05fe42e7a21664bed22ed182523720083</citedby><cites>FETCH-LOGICAL-c385t-36f7b9cd47399eaba566b2ec5e477a3b05fe42e7a21664bed22ed182523720083</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4400031$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4400031$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=20187828$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Kosta, G.</creatorcontrib><creatorcontrib>Benoit, M.</creatorcontrib><title>Group Behavior Recognition for Gesture Analysis</title><title>IEEE transactions on circuits and systems for video technology</title><addtitle>TCSVT</addtitle><description>This paper analyzes the movements of the human body limbs (hands, feet and head) and center of gravity in order to detect and analyze simple actions such as walking and running. We propose a novel vision of the human body, by considering the limbs as cooperative agents that form a hierarchy of cooperative teams: the whole body. The movements are analyzed at individual level and at team level using a modular hierarchical structure. Knowledge of the high-level team actions (such as ldquowalkingrdquo) improves the pertinence of our predictions on the low-level individual actions (foot is moving back and forth) and allows us to compensate for missing or noisy data produced by the feature extraction system. In terms of group behavior recognition, we propose a novel framework for online probabilistic plan recognition in cooperative multiagent systems: the Multiagent Hidden Markov mEmory Model (M-AHMEM), which is a dynamic Bayesian network. Experiments on an existing video database using different models of the human body show the feasibility of the approach.</description><subject>Applied sciences</subject><subject>Bayesian methods</subject><subject>Bayesian network</subject><subject>Biological system modeling</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>dynamic</subject><subject>Dynamical systems</subject><subject>Dynamics</subject><subject>Exact sciences and technology</subject><subject>Feature extraction</subject><subject>Foot</subject><subject>Gravity</subject><subject>group behavior</subject><subject>Hidden Markov models</subject><subject>human action</subject><subject>Human body</subject><subject>Humans</subject><subject>Information, signal and communications theory</subject><subject>Legged locomotion</subject><subject>Limbs</subject><subject>Mathematical models</subject><subject>Miscellaneous</subject><subject>Movements</subject><subject>Multiagent systems</subject><subject>particle filter</subject><subject>plan recognition</subject><subject>Rao-Blackwell</subject><subject>Recognition</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal, noise</subject><subject>Spatial databases</subject><subject>Studies</subject><subject>Telecommunications and information theory</subject><issn>1051-8215</issn><issn>1558-2205</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kEFLw0AQhRdRsFZ_gHgJgnpKuzu7m909atEqFAStXpdNOtGUNKm7jdB_b2JKDx48zQzzvWHeI-Sc0RFj1Iznk9f3-QgoVSPDuEn0ARkwKXUMQOVh21PJYg1MHpOTEJaUMqGFGpDx1NfNOrrDT_dd1D56waz-qIpNUVdR3s5TDJvGY3RbuXIbinBKjnJXBjzb1SF5e7ifTx7j2fP0aXI7izOu5SbmSa5Sky2E4sagS51MkhQwkyiUcjylMkcBqBywJBEpLgBwwTRI4Ko1ofmQ3PR3177-aton7KoIGZalq7BugtVKUsMMFy15_S_JhTAqgaQFL_-Ay7rxra9gDQPQAkwHsR7KfB2Cx9yufbFyfmsZtV3S9jdp2yVt-6RbzdXusAuZK3PvqqwIeyFQppWGjrvouQIR92shKKWc8R83RoTT</recordid><startdate>20080201</startdate><enddate>20080201</enddate><creator>Kosta, G.</creator><creator>Benoit, M.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>20080201</creationdate><title>Group Behavior Recognition for Gesture Analysis</title><author>Kosta, G. ; Benoit, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c385t-36f7b9cd47399eaba566b2ec5e477a3b05fe42e7a21664bed22ed182523720083</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Applied sciences</topic><topic>Bayesian methods</topic><topic>Bayesian network</topic><topic>Biological system modeling</topic><topic>Detection, estimation, filtering, equalization, prediction</topic><topic>dynamic</topic><topic>Dynamical systems</topic><topic>Dynamics</topic><topic>Exact sciences and technology</topic><topic>Feature extraction</topic><topic>Foot</topic><topic>Gravity</topic><topic>group behavior</topic><topic>Hidden Markov models</topic><topic>human action</topic><topic>Human body</topic><topic>Humans</topic><topic>Information, signal and communications theory</topic><topic>Legged locomotion</topic><topic>Limbs</topic><topic>Mathematical models</topic><topic>Miscellaneous</topic><topic>Movements</topic><topic>Multiagent systems</topic><topic>particle filter</topic><topic>plan recognition</topic><topic>Rao-Blackwell</topic><topic>Recognition</topic><topic>Signal and communications theory</topic><topic>Signal processing</topic><topic>Signal, noise</topic><topic>Spatial databases</topic><topic>Studies</topic><topic>Telecommunications and information theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kosta, G.</creatorcontrib><creatorcontrib>Benoit, M.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on circuits and systems for video technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kosta, G.</au><au>Benoit, M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Group Behavior Recognition for Gesture Analysis</atitle><jtitle>IEEE transactions on circuits and systems for video technology</jtitle><stitle>TCSVT</stitle><date>2008-02-01</date><risdate>2008</risdate><volume>18</volume><issue>2</issue><spage>211</spage><epage>222</epage><pages>211-222</pages><issn>1051-8215</issn><eissn>1558-2205</eissn><coden>ITCTEM</coden><abstract>This paper analyzes the movements of the human body limbs (hands, feet and head) and center of gravity in order to detect and analyze simple actions such as walking and running. We propose a novel vision of the human body, by considering the limbs as cooperative agents that form a hierarchy of cooperative teams: the whole body. The movements are analyzed at individual level and at team level using a modular hierarchical structure. Knowledge of the high-level team actions (such as ldquowalkingrdquo) improves the pertinence of our predictions on the low-level individual actions (foot is moving back and forth) and allows us to compensate for missing or noisy data produced by the feature extraction system. In terms of group behavior recognition, we propose a novel framework for online probabilistic plan recognition in cooperative multiagent systems: the Multiagent Hidden Markov mEmory Model (M-AHMEM), which is a dynamic Bayesian network. Experiments on an existing video database using different models of the human body show the feasibility of the approach.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TCSVT.2007.913968</doi><tpages>12</tpages></addata></record> |
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| ispartof | IEEE transactions on circuits and systems for video technology, 2008-02, Vol.18 (2), p.211-222 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Applied sciences Bayesian methods Bayesian network Biological system modeling Detection, estimation, filtering, equalization, prediction dynamic Dynamical systems Dynamics Exact sciences and technology Feature extraction Foot Gravity group behavior Hidden Markov models human action Human body Humans Information, signal and communications theory Legged locomotion Limbs Mathematical models Miscellaneous Movements Multiagent systems particle filter plan recognition Rao-Blackwell Recognition Signal and communications theory Signal processing Signal, noise Spatial databases Studies Telecommunications and information theory |
| title | Group Behavior Recognition for Gesture Analysis |
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