Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition
A tool called Belief Scheduler is proposed for state sequence recognition in the Transferable Belief Model (TBM) framework. This tool makes noisy temporal belief functions smoother using a Temporal Evidential Filter (TEF). The Belief Scheduler makes belief on states smoother, separates the states (a...
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| Veröffentlicht in: | International journal of approximate reasoning 2010-09, Vol.51 (7), p.846-865 |
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| container_issue | 7 |
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| container_title | International journal of approximate reasoning |
| container_volume | 51 |
| creator | Ramasso, E. Panagiotakis, C. Rombaut, M. Pellerin, D. |
| description | A tool called Belief Scheduler is proposed for state sequence recognition in the Transferable Belief Model (TBM) framework. This tool makes noisy temporal belief functions smoother using a Temporal Evidential Filter (TEF). The Belief Scheduler makes belief on states smoother, separates the states (assumed to be
true or
false) and synchronizes them in order to infer the sequence. A criterion is also provided to assess the appropriateness between observed belief functions and a given sequence model. This criterion is based on the conflict information appearing explicitly in the TBM when combining observed belief functions with predictions. The Belief Scheduler is part of a generic architecture developed for on-line and automatic human action and activity recognition in videos of athletics taken with a moving camera. In experiments, the system is assessed on a database composed of 69 real athletics video sequences. The goal is to automatically recognize
running,
jumping,
falling and
standing-up actions as well as
high jump,
pole vault,
triple jump and
long jump activities of an athlete. A comparison with Hidden Markov Models for video classification is also provided. |
| doi_str_mv | 10.1016/j.ijar.2010.04.005 |
| format | Article |
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true or
false) and synchronizes them in order to infer the sequence. A criterion is also provided to assess the appropriateness between observed belief functions and a given sequence model. This criterion is based on the conflict information appearing explicitly in the TBM when combining observed belief functions with predictions. The Belief Scheduler is part of a generic architecture developed for on-line and automatic human action and activity recognition in videos of athletics taken with a moving camera. In experiments, the system is assessed on a database composed of 69 real athletics video sequences. The goal is to automatically recognize
running,
jumping,
falling and
standing-up actions as well as
high jump,
pole vault,
triple jump and
long jump activities of an athlete. A comparison with Hidden Markov Models for video classification is also provided.</description><identifier>ISSN: 0888-613X</identifier><identifier>EISSN: 1873-4731</identifier><identifier>DOI: 10.1016/j.ijar.2010.04.005</identifier><identifier>CODEN: IJARE4</identifier><language>eng</language><publisher>Amsterdam: Elsevier Inc</publisher><subject>Applied sciences ; Artificial intelligence ; Belief finite state machine ; Computer Science ; Computer science; control theory; systems ; Conflict ; Engineering Sciences ; Exact sciences and technology ; Human motion analysis ; Learning and adaptive systems ; Pattern recognition. Digital image processing. Computational geometry ; Sequence recognition ; Signal and Image Processing ; Temporal Evidential Filter ; Transferable Belief Model</subject><ispartof>International journal of approximate reasoning, 2010-09, Vol.51 (7), p.846-865</ispartof><rights>2010 Elsevier Inc.</rights><rights>2015 INIST-CNRS</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-f1762b1b1aab624abe6216263633f4679614ca36481fc552346c73c15011856c3</citedby><cites>FETCH-LOGICAL-c408t-f1762b1b1aab624abe6216263633f4679614ca36481fc552346c73c15011856c3</cites><orcidid>0000-0002-3792-1706 ; 0000-0003-4633-8501</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ijar.2010.04.005$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23072757$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-00475787$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Ramasso, E.</creatorcontrib><creatorcontrib>Panagiotakis, C.</creatorcontrib><creatorcontrib>Rombaut, M.</creatorcontrib><creatorcontrib>Pellerin, D.</creatorcontrib><title>Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition</title><title>International journal of approximate reasoning</title><description>A tool called Belief Scheduler is proposed for state sequence recognition in the Transferable Belief Model (TBM) framework. This tool makes noisy temporal belief functions smoother using a Temporal Evidential Filter (TEF). The Belief Scheduler makes belief on states smoother, separates the states (assumed to be
true or
false) and synchronizes them in order to infer the sequence. A criterion is also provided to assess the appropriateness between observed belief functions and a given sequence model. This criterion is based on the conflict information appearing explicitly in the TBM when combining observed belief functions with predictions. The Belief Scheduler is part of a generic architecture developed for on-line and automatic human action and activity recognition in videos of athletics taken with a moving camera. In experiments, the system is assessed on a database composed of 69 real athletics video sequences. The goal is to automatically recognize
running,
jumping,
falling and
standing-up actions as well as
high jump,
pole vault,
triple jump and
long jump activities of an athlete. A comparison with Hidden Markov Models for video classification is also provided.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Belief finite state machine</subject><subject>Computer Science</subject><subject>Computer science; control theory; systems</subject><subject>Conflict</subject><subject>Engineering Sciences</subject><subject>Exact sciences and technology</subject><subject>Human motion analysis</subject><subject>Learning and adaptive systems</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Sequence recognition</subject><subject>Signal and Image Processing</subject><subject>Temporal Evidential Filter</subject><subject>Transferable Belief Model</subject><issn>0888-613X</issn><issn>1873-4731</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNp9kMFu1DAQhi0EEkvhBTj5woFDwozt2Ebisq0oRVrEoa3EzXKcCeslmyx2dlHfvg6LeuRk6ff3zWh-xt4i1AioP-zquPOpFlACUDVA84yt0BpZKSPxOVuBtbbSKH-8ZK9y3gGANsqu2O9LGiL1_DZsqTsOlHjrM3V8Gvl-6mjgvY_DMRHvaKYwx5LHkc9b4neX33if_J7-TOlXzdeHwxCD_0vME98e937kvhinOD_wRGH6Ocbl9zV70fsh05t_7wW7v_58d3VTbb5_-Xq13lRBgZ2rHo0WLbbofauF8i1pgVpoqaXslTYfNargpVYW-9A0QiodjAzYAKJtdJAX7P157tYP7pDi3qcHN_nobtYbt2QAyjTGmhMWVpzZkKacE_VPAoJbCnY7txTsloIdqOI2RXp3lg4-Bz-ULsYQ85MpJBhRFhTu05mjcu0pUnI5RBoDdbHUMrtuiv9b8wjffJBm</recordid><startdate>20100901</startdate><enddate>20100901</enddate><creator>Ramasso, E.</creator><creator>Panagiotakis, C.</creator><creator>Rombaut, M.</creator><creator>Pellerin, D.</creator><general>Elsevier Inc</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-3792-1706</orcidid><orcidid>https://orcid.org/0000-0003-4633-8501</orcidid></search><sort><creationdate>20100901</creationdate><title>Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition</title><author>Ramasso, E. ; Panagiotakis, C. ; Rombaut, M. ; Pellerin, D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-f1762b1b1aab624abe6216263633f4679614ca36481fc552346c73c15011856c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Belief finite state machine</topic><topic>Computer Science</topic><topic>Computer science; control theory; systems</topic><topic>Conflict</topic><topic>Engineering Sciences</topic><topic>Exact sciences and technology</topic><topic>Human motion analysis</topic><topic>Learning and adaptive systems</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Sequence recognition</topic><topic>Signal and Image Processing</topic><topic>Temporal Evidential Filter</topic><topic>Transferable Belief Model</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ramasso, E.</creatorcontrib><creatorcontrib>Panagiotakis, C.</creatorcontrib><creatorcontrib>Rombaut, M.</creatorcontrib><creatorcontrib>Pellerin, D.</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>International journal of approximate reasoning</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ramasso, E.</au><au>Panagiotakis, C.</au><au>Rombaut, M.</au><au>Pellerin, D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition</atitle><jtitle>International journal of approximate reasoning</jtitle><date>2010-09-01</date><risdate>2010</risdate><volume>51</volume><issue>7</issue><spage>846</spage><epage>865</epage><pages>846-865</pages><issn>0888-613X</issn><eissn>1873-4731</eissn><coden>IJARE4</coden><abstract>A tool called Belief Scheduler is proposed for state sequence recognition in the Transferable Belief Model (TBM) framework. This tool makes noisy temporal belief functions smoother using a Temporal Evidential Filter (TEF). The Belief Scheduler makes belief on states smoother, separates the states (assumed to be
true or
false) and synchronizes them in order to infer the sequence. A criterion is also provided to assess the appropriateness between observed belief functions and a given sequence model. This criterion is based on the conflict information appearing explicitly in the TBM when combining observed belief functions with predictions. The Belief Scheduler is part of a generic architecture developed for on-line and automatic human action and activity recognition in videos of athletics taken with a moving camera. In experiments, the system is assessed on a database composed of 69 real athletics video sequences. The goal is to automatically recognize
running,
jumping,
falling and
standing-up actions as well as
high jump,
pole vault,
triple jump and
long jump activities of an athlete. A comparison with Hidden Markov Models for video classification is also provided.</abstract><cop>Amsterdam</cop><pub>Elsevier Inc</pub><doi>10.1016/j.ijar.2010.04.005</doi><tpages>20</tpages><orcidid>https://orcid.org/0000-0002-3792-1706</orcidid><orcidid>https://orcid.org/0000-0003-4633-8501</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | International journal of approximate reasoning, 2010-09, Vol.51 (7), p.846-865 |
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| source | Access via ScienceDirect (Elsevier); EZB-FREE-00999 freely available EZB journals |
| subjects | Applied sciences Artificial intelligence Belief finite state machine Computer Science Computer science control theory systems Conflict Engineering Sciences Exact sciences and technology Human motion analysis Learning and adaptive systems Pattern recognition. Digital image processing. Computational geometry Sequence recognition Signal and Image Processing Temporal Evidential Filter Transferable Belief Model |
| title | Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition |
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