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Change Detection in a 3-d World
This paper examines the problem of detecting changes in a 3-d scene from a sequence of images, taken by cameras with arbitrary but known pose. No prior knowledge of the state of normal appearance and geometry of object surfaces is assumed, and abnormal changes can occur in any image of the sequence....
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creator | Pollard, T. Mundy, J.L. |
description | This paper examines the problem of detecting changes in a 3-d scene from a sequence of images, taken by cameras with arbitrary but known pose. No prior knowledge of the state of normal appearance and geometry of object surfaces is assumed, and abnormal changes can occur in any image of the sequence. To the authors' knowledge, this paper is the first to address the change detection problem in such a general framework. Existing change detection algorithms that exploit multiple image viewpoints typically can detect only motion changes or assume a planar world geometry which cannot cope effectively with appearance changes due to occlusion and un-modeled 3-d scene geometry (ego-motion parallax). The approach presented here can manage the complications of unknown and sometimes changing world surfaces by maintaining a 3-d voxel-based model, where probability distributions for surface occupancy and image appearance are stored in each voxel. The probability distributions at each voxel are continuously updated as new images are received. The key question of convergence of this joint estimation problem is answered by a formal proof based on realistic assumptions about the nature of real world scenes. A series of experiments are presented that evaluate change detection accuracy under laboratory-controlled conditions as well as aerial reconnaissance scenarios.- |
doi_str_mv | 10.1109/CVPR.2007.383073 |
format | conference_proceeding |
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No prior knowledge of the state of normal appearance and geometry of object surfaces is assumed, and abnormal changes can occur in any image of the sequence. To the authors' knowledge, this paper is the first to address the change detection problem in such a general framework. Existing change detection algorithms that exploit multiple image viewpoints typically can detect only motion changes or assume a planar world geometry which cannot cope effectively with appearance changes due to occlusion and un-modeled 3-d scene geometry (ego-motion parallax). The approach presented here can manage the complications of unknown and sometimes changing world surfaces by maintaining a 3-d voxel-based model, where probability distributions for surface occupancy and image appearance are stored in each voxel. The probability distributions at each voxel are continuously updated as new images are received. The key question of convergence of this joint estimation problem is answered by a formal proof based on realistic assumptions about the nature of real world scenes. A series of experiments are presented that evaluate change detection accuracy under laboratory-controlled conditions as well as aerial reconnaissance scenarios.-</description><identifier>ISSN: 1063-6919</identifier><identifier>ISBN: 9781424411795</identifier><identifier>ISBN: 1424411793</identifier><identifier>EISBN: 1424411807</identifier><identifier>EISBN: 9781424411801</identifier><identifier>DOI: 10.1109/CVPR.2007.383073</identifier><language>eng</language><publisher>IEEE</publisher><subject>Cameras ; Change detection algorithms ; Detection algorithms ; Image resolution ; Information geometry ; Layout ; Lighting ; Motion detection ; Object detection ; Probability distribution</subject><ispartof>2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007, p.1-6</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/4270098$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27924,54919</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4270098$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Pollard, T.</creatorcontrib><creatorcontrib>Mundy, J.L.</creatorcontrib><title>Change Detection in a 3-d World</title><title>2007 IEEE Conference on Computer Vision and Pattern Recognition</title><addtitle>CVPR</addtitle><description>This paper examines the problem of detecting changes in a 3-d scene from a sequence of images, taken by cameras with arbitrary but known pose. No prior knowledge of the state of normal appearance and geometry of object surfaces is assumed, and abnormal changes can occur in any image of the sequence. To the authors' knowledge, this paper is the first to address the change detection problem in such a general framework. Existing change detection algorithms that exploit multiple image viewpoints typically can detect only motion changes or assume a planar world geometry which cannot cope effectively with appearance changes due to occlusion and un-modeled 3-d scene geometry (ego-motion parallax). The approach presented here can manage the complications of unknown and sometimes changing world surfaces by maintaining a 3-d voxel-based model, where probability distributions for surface occupancy and image appearance are stored in each voxel. The probability distributions at each voxel are continuously updated as new images are received. The key question of convergence of this joint estimation problem is answered by a formal proof based on realistic assumptions about the nature of real world scenes. A series of experiments are presented that evaluate change detection accuracy under laboratory-controlled conditions as well as aerial reconnaissance scenarios.-</description><subject>Cameras</subject><subject>Change detection algorithms</subject><subject>Detection algorithms</subject><subject>Image resolution</subject><subject>Information geometry</subject><subject>Layout</subject><subject>Lighting</subject><subject>Motion detection</subject><subject>Object detection</subject><subject>Probability distribution</subject><issn>1063-6919</issn><isbn>9781424411795</isbn><isbn>1424411793</isbn><isbn>1424411807</isbn><isbn>9781424411801</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotjU1Lw0AURUdUsK3ZCy6cPzDxvfnIzFtKrFooKFJ0WaaZNxqpqSTZ-O8N2Lu5HLicK8QVQokIdFu_vbyWGsCXJhjw5kTM0WprEQP4U1GQD0f25M7EDKEyqiKkC1EMwxdMCdPUhZm4qT9j98HynkduxvbQybaTURqV5Puh36dLcZ7jfuDi2AuxeVhu6ie1fn5c1Xdr1RKMSudI0e84MTFmNtN_yhUlD1pHMk12bGMk0MFol9lxsI4qyClhCA3uzEJc_2tbZt7-9O137H-3VnsACuYPHiU-1Q</recordid><startdate>200706</startdate><enddate>200706</enddate><creator>Pollard, T.</creator><creator>Mundy, J.L.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200706</creationdate><title>Change Detection in a 3-d World</title><author>Pollard, T. ; Mundy, J.L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-2fa9a7bede9e1fe3781df69d7022a93cf5e4aa9028325fe5e845960fdd188c1b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Cameras</topic><topic>Change detection algorithms</topic><topic>Detection algorithms</topic><topic>Image resolution</topic><topic>Information geometry</topic><topic>Layout</topic><topic>Lighting</topic><topic>Motion detection</topic><topic>Object detection</topic><topic>Probability distribution</topic><toplevel>online_resources</toplevel><creatorcontrib>Pollard, T.</creatorcontrib><creatorcontrib>Mundy, J.L.</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>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>Pollard, T.</au><au>Mundy, J.L.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Change Detection in a 3-d World</atitle><btitle>2007 IEEE Conference on Computer Vision and Pattern Recognition</btitle><stitle>CVPR</stitle><date>2007-06</date><risdate>2007</risdate><spage>1</spage><epage>6</epage><pages>1-6</pages><issn>1063-6919</issn><isbn>9781424411795</isbn><isbn>1424411793</isbn><eisbn>1424411807</eisbn><eisbn>9781424411801</eisbn><abstract>This paper examines the problem of detecting changes in a 3-d scene from a sequence of images, taken by cameras with arbitrary but known pose. No prior knowledge of the state of normal appearance and geometry of object surfaces is assumed, and abnormal changes can occur in any image of the sequence. To the authors' knowledge, this paper is the first to address the change detection problem in such a general framework. Existing change detection algorithms that exploit multiple image viewpoints typically can detect only motion changes or assume a planar world geometry which cannot cope effectively with appearance changes due to occlusion and un-modeled 3-d scene geometry (ego-motion parallax). The approach presented here can manage the complications of unknown and sometimes changing world surfaces by maintaining a 3-d voxel-based model, where probability distributions for surface occupancy and image appearance are stored in each voxel. The probability distributions at each voxel are continuously updated as new images are received. The key question of convergence of this joint estimation problem is answered by a formal proof based on realistic assumptions about the nature of real world scenes. A series of experiments are presented that evaluate change detection accuracy under laboratory-controlled conditions as well as aerial reconnaissance scenarios.-</abstract><pub>IEEE</pub><doi>10.1109/CVPR.2007.383073</doi><tpages>6</tpages></addata></record> |
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identifier | ISSN: 1063-6919 |
ispartof | 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007, p.1-6 |
issn | 1063-6919 |
language | eng |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Cameras Change detection algorithms Detection algorithms Image resolution Information geometry Layout Lighting Motion detection Object detection Probability distribution |
title | Change Detection in a 3-d World |
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