A Decision Fusion and Reasoning Module for a Traffic Sign Recognition System
A novel approach for a decision fusion and reasoning system for vision-based traffic sign recognition is presented. This module consists of several steps. In the first stage, a track-based Bayesian fusion scheme is used to fuse the classification results from each frame to obtain a fusion result for...
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| Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2011-12, Vol.12 (4), p.1126-1134 |
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| container_title | IEEE transactions on intelligent transportation systems |
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| creator | Meuter, M. Nunn, C. Gormer, S. M. Muller-Schneiders, S. Kummert, A. |
| description | A novel approach for a decision fusion and reasoning system for vision-based traffic sign recognition is presented. This module consists of several steps. In the first stage, a track-based Bayesian fusion scheme is used to fuse the classification results from each frame to obtain a fusion result for each track to decide whether a sign is present, as well as to determine the sign type. In order to determine the sign type, the temporal fusion scheme has been combined with a decision tree. In the second stage, the system combines and fuses probable identical objects which help to further reduce failures in the recognition process. The decision is based on the fusion results, as well as a position cue. Finally, a reasoning module is used to decide which of the passed signs should be shown to the driver. In addition to these modules, a general evaluation method for multi-class tracking systems is shown. While some failures are observed from the evaluation on object level, the additional post processing steps improve the system in such a way that the finally presented signs are almost always correct on the test set. |
| doi_str_mv | 10.1109/TITS.2011.2157497 |
| format | Article |
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M. ; Muller-Schneiders, S. ; Kummert, A.</creator><creatorcontrib>Meuter, M. ; Nunn, C. ; Gormer, S. M. ; Muller-Schneiders, S. ; Kummert, A.</creatorcontrib><description>A novel approach for a decision fusion and reasoning system for vision-based traffic sign recognition is presented. This module consists of several steps. In the first stage, a track-based Bayesian fusion scheme is used to fuse the classification results from each frame to obtain a fusion result for each track to decide whether a sign is present, as well as to determine the sign type. In order to determine the sign type, the temporal fusion scheme has been combined with a decision tree. In the second stage, the system combines and fuses probable identical objects which help to further reduce failures in the recognition process. The decision is based on the fusion results, as well as a position cue. Finally, a reasoning module is used to decide which of the passed signs should be shown to the driver. In addition to these modules, a general evaluation method for multi-class tracking systems is shown. While some failures are observed from the evaluation on object level, the additional post processing steps improve the system in such a way that the finally presented signs are almost always correct on the test set.</description><identifier>ISSN: 1524-9050</identifier><identifier>EISSN: 1558-0016</identifier><identifier>DOI: 10.1109/TITS.2011.2157497</identifier><identifier>CODEN: ITISFG</identifier><language>eng</language><publisher>Piscataway, NJ: IEEE</publisher><subject>Active safety ; Applied sciences ; Artificial intelligence ; Bayesian methods ; Computer science; control theory; systems ; decision fusion ; Decision theory. Utility theory ; Decision trees ; Drivers ; evaluation ; Exact sciences and technology ; Failure ; Fuses ; Image classification ; image processing ; Modules ; moving host ; Operational research and scientific management ; Operational research. Management science ; Pattern recognition. Digital image processing. Computational geometry ; Reasoning ; Recognition ; Tracking ; traffic sign recognition ; Traffic signs</subject><ispartof>IEEE transactions on intelligent transportation systems, 2011-12, Vol.12 (4), p.1126-1134</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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M.</creatorcontrib><creatorcontrib>Muller-Schneiders, S.</creatorcontrib><creatorcontrib>Kummert, A.</creatorcontrib><title>A Decision Fusion and Reasoning Module for a Traffic Sign Recognition System</title><title>IEEE transactions on intelligent transportation systems</title><addtitle>TITS</addtitle><description>A novel approach for a decision fusion and reasoning system for vision-based traffic sign recognition is presented. This module consists of several steps. In the first stage, a track-based Bayesian fusion scheme is used to fuse the classification results from each frame to obtain a fusion result for each track to decide whether a sign is present, as well as to determine the sign type. In order to determine the sign type, the temporal fusion scheme has been combined with a decision tree. In the second stage, the system combines and fuses probable identical objects which help to further reduce failures in the recognition process. The decision is based on the fusion results, as well as a position cue. Finally, a reasoning module is used to decide which of the passed signs should be shown to the driver. In addition to these modules, a general evaluation method for multi-class tracking systems is shown. While some failures are observed from the evaluation on object level, the additional post processing steps improve the system in such a way that the finally presented signs are almost always correct on the test set.</description><subject>Active safety</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Bayesian methods</subject><subject>Computer science; control theory; systems</subject><subject>decision fusion</subject><subject>Decision theory. Utility theory</subject><subject>Decision trees</subject><subject>Drivers</subject><subject>evaluation</subject><subject>Exact sciences and technology</subject><subject>Failure</subject><subject>Fuses</subject><subject>Image classification</subject><subject>image processing</subject><subject>Modules</subject><subject>moving host</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Reasoning</subject><subject>Recognition</subject><subject>Tracking</subject><subject>traffic sign recognition</subject><subject>Traffic signs</subject><issn>1524-9050</issn><issn>1558-0016</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1LAzEQhhdRUKs_QLwsguBla2bzscmx1E-oCLaeQzZNSso2qUn34L83a4sHTzPMPO8wPEVxBWgMgMT94nUxH9cIYFwDbYhojoozoJRXCAE7HvqaVAJRdFqcp7TOU0IBzorZpHww2iUXfPnU_xbll-WHUSl451flW1j2nSltiKUqF1FZ63Q5dyufGR1W3u2GzPw77czmojixqkvm8lBHxefT42L6Us3en1-nk1mlSQ27ijStMAjbGimF8ZJzwIrXnGjG8stNi5GGttWiFQxjDE3eobYmjFlMCbUMj4q7_d1tDF-9STu5cUmbrlPehD5JYA1gjjjhGb35h65DH33-TgrEaHYgRIZgD-kYUorGym10GxW_JSA56JWDXjnolQe9OXN7OKySVp2NymeNf8GaYiyIQJm73nPOGPO3pgIQ5hT_AO9igBI</recordid><startdate>20111201</startdate><enddate>20111201</enddate><creator>Meuter, M.</creator><creator>Nunn, C.</creator><creator>Gormer, S. M.</creator><creator>Muller-Schneiders, S.</creator><creator>Kummert, A.</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>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope></search><sort><creationdate>20111201</creationdate><title>A Decision Fusion and Reasoning Module for a Traffic Sign Recognition System</title><author>Meuter, M. ; Nunn, C. ; Gormer, S. M. ; Muller-Schneiders, S. ; Kummert, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c421t-47b9e03f20aa33d8813a8284c661557b30c1bbc9b96333178280b2466f3545f63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Active safety</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Bayesian methods</topic><topic>Computer science; control theory; systems</topic><topic>decision fusion</topic><topic>Decision theory. Utility theory</topic><topic>Decision trees</topic><topic>Drivers</topic><topic>evaluation</topic><topic>Exact sciences and technology</topic><topic>Failure</topic><topic>Fuses</topic><topic>Image classification</topic><topic>image processing</topic><topic>Modules</topic><topic>moving host</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Reasoning</topic><topic>Recognition</topic><topic>Tracking</topic><topic>traffic sign recognition</topic><topic>Traffic signs</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Meuter, M.</creatorcontrib><creatorcontrib>Nunn, C.</creatorcontrib><creatorcontrib>Gormer, S. M.</creatorcontrib><creatorcontrib>Muller-Schneiders, S.</creatorcontrib><creatorcontrib>Kummert, A.</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>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</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><jtitle>IEEE transactions on intelligent transportation systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Meuter, M.</au><au>Nunn, C.</au><au>Gormer, S. M.</au><au>Muller-Schneiders, S.</au><au>Kummert, A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Decision Fusion and Reasoning Module for a Traffic Sign Recognition System</atitle><jtitle>IEEE transactions on intelligent transportation systems</jtitle><stitle>TITS</stitle><date>2011-12-01</date><risdate>2011</risdate><volume>12</volume><issue>4</issue><spage>1126</spage><epage>1134</epage><pages>1126-1134</pages><issn>1524-9050</issn><eissn>1558-0016</eissn><coden>ITISFG</coden><abstract>A novel approach for a decision fusion and reasoning system for vision-based traffic sign recognition is presented. This module consists of several steps. In the first stage, a track-based Bayesian fusion scheme is used to fuse the classification results from each frame to obtain a fusion result for each track to decide whether a sign is present, as well as to determine the sign type. In order to determine the sign type, the temporal fusion scheme has been combined with a decision tree. In the second stage, the system combines and fuses probable identical objects which help to further reduce failures in the recognition process. The decision is based on the fusion results, as well as a position cue. Finally, a reasoning module is used to decide which of the passed signs should be shown to the driver. In addition to these modules, a general evaluation method for multi-class tracking systems is shown. While some failures are observed from the evaluation on object level, the additional post processing steps improve the system in such a way that the finally presented signs are almost always correct on the test set.</abstract><cop>Piscataway, NJ</cop><pub>IEEE</pub><doi>10.1109/TITS.2011.2157497</doi><tpages>9</tpages></addata></record> |
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| subjects | Active safety Applied sciences Artificial intelligence Bayesian methods Computer science control theory systems decision fusion Decision theory. Utility theory Decision trees Drivers evaluation Exact sciences and technology Failure Fuses Image classification image processing Modules moving host Operational research and scientific management Operational research. Management science Pattern recognition. Digital image processing. Computational geometry Reasoning Recognition Tracking traffic sign recognition Traffic signs |
| title | A Decision Fusion and Reasoning Module for a Traffic Sign Recognition System |
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