Centralized fusion for fast people detection in dense environment
Human beings do not have well defined shapes neither well defined behaviors. In dense outdoor environments, they are as a consequence hard to detect and algorithms based on a single sensor tend to produce lot of wrong detections. Moreover, many applications require algorithms that work very fast on...
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| creator | Gate, G. Breheret, A. Nashashibi, F. |
| description | Human beings do not have well defined shapes neither well defined behaviors. In dense outdoor environments, they are as a consequence hard to detect and algorithms based on a single sensor tend to produce lot of wrong detections. Moreover, many applications require algorithms that work very fast on CPU limited mobile architectures while remaining able to detect, track and classify objects as people with a very high precision. We present an algorithm based on the contribution of a range finder and a vision based algorithm that addresses these three constraints: efficiency, velocity and robustness and that we believe is scalable to a large variety of applications. |
| doi_str_mv | 10.1109/ROBOT.2009.5152645 |
| format | Conference Proceeding |
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We present an algorithm based on the contribution of a range finder and a vision based algorithm that addresses these three constraints: efficiency, velocity and robustness and that we believe is scalable to a large variety of applications.</description><subject>Bayesian methods</subject><subject>Boosting</subject><subject>Cameras</subject><subject>Data fusion</subject><subject>Detection algorithms</subject><subject>Humans</subject><subject>Laser fusion</subject><subject>People detection</subject><subject>Recursive estimation</subject><subject>Robots</subject><subject>Robustness</subject><subject>Shape</subject><subject>Target tracking</subject><issn>1050-4729</issn><issn>2577-087X</issn><isbn>1424427886</isbn><isbn>9781424427888</isbn><isbn>1424427894</isbn><isbn>9781424427895</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkNtKw0AURcdLwVj7A_qSH0g8czlzeazBGxQCUsG3kkzOQCRNQiYK-vVWLPi02SzWftiMXXPIOQd3-1LeldtcALgcOQqt8IRdciWUEsY6dcoSgcZkYM3b2T-w-pwlHBAyZYRbsMRBphVwtBdsFeM7AHCjleQyYeuC-nmquvabmjR8xHbo0zBMaajinI40jB2lDc3k51_S9ofSR0qp_2ynod8f5Cu2CFUXaXXMJXt9uN8WT9mmfHwu1pvMC8A5017JgMJBY0lrpY0PaEODWCunhEAPlfSGagPO1rb2srIIBILAkml0LZfs5m-3JaLdOLX7avraHW-RP6fvUG0</recordid><startdate>200905</startdate><enddate>200905</enddate><creator>Gate, G.</creator><creator>Breheret, A.</creator><creator>Nashashibi, F.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200905</creationdate><title>Centralized fusion for fast people detection in dense environment</title><author>Gate, G. ; Breheret, A. ; Nashashibi, F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c205t-6c43f5290d8e66467cf58fd55b494225c0a3c7eb7098b8bc3a850e02e08e7d6b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Bayesian methods</topic><topic>Boosting</topic><topic>Cameras</topic><topic>Data fusion</topic><topic>Detection algorithms</topic><topic>Humans</topic><topic>Laser fusion</topic><topic>People detection</topic><topic>Recursive estimation</topic><topic>Robots</topic><topic>Robustness</topic><topic>Shape</topic><topic>Target tracking</topic><toplevel>online_resources</toplevel><creatorcontrib>Gate, G.</creatorcontrib><creatorcontrib>Breheret, A.</creatorcontrib><creatorcontrib>Nashashibi, F.</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 (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Gate, G.</au><au>Breheret, A.</au><au>Nashashibi, F.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Centralized fusion for fast people detection in dense environment</atitle><btitle>2009 IEEE International Conference on Robotics and Automation</btitle><stitle>ROBOT</stitle><date>2009-05</date><risdate>2009</risdate><spage>76</spage><epage>81</epage><pages>76-81</pages><issn>1050-4729</issn><eissn>2577-087X</eissn><isbn>1424427886</isbn><isbn>9781424427888</isbn><eisbn>1424427894</eisbn><eisbn>9781424427895</eisbn><abstract>Human beings do not have well defined shapes neither well defined behaviors. In dense outdoor environments, they are as a consequence hard to detect and algorithms based on a single sensor tend to produce lot of wrong detections. Moreover, many applications require algorithms that work very fast on CPU limited mobile architectures while remaining able to detect, track and classify objects as people with a very high precision. We present an algorithm based on the contribution of a range finder and a vision based algorithm that addresses these three constraints: efficiency, velocity and robustness and that we believe is scalable to a large variety of applications.</abstract><pub>IEEE</pub><doi>10.1109/ROBOT.2009.5152645</doi><tpages>6</tpages></addata></record> |
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| ispartof | 2009 IEEE International Conference on Robotics and Automation, 2009, p.76-81 |
| issn | 1050-4729 2577-087X |
| language | eng |
| recordid | cdi_ieee_primary_5152645 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Bayesian methods Boosting Cameras Data fusion Detection algorithms Humans Laser fusion People detection Recursive estimation Robots Robustness Shape Target tracking |
| title | Centralized fusion for fast people detection in dense environment |
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