Improved variational inference for tracking in clutter
We apply the expectation propagation (EP) algorithm to temporally track targets using sensors that produce spurious clutter detections, and may sometimes fail to detect the true target. The variational inference framework underlying EP allows the tracker to be easily adapted to varying measurement m...
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| creator | Pacheco, J. L. Sudderth, E. B. |
| description | We apply the expectation propagation (EP) algorithm to temporally track targets using sensors that produce spurious clutter detections, and may sometimes fail to detect the true target. The variational inference framework underlying EP allows the tracker to be easily adapted to varying measurement models. We develop variants of EP based on single Gaussian and Gaussian mixture approximations of posterior target location distributions, which offer a tradeoff between accuracy and computational complexity. Experiments show improved tracking accuracy and uncertainty estimation relative to widely used baseline tracking algorithms. |
| doi_str_mv | 10.1109/SSP.2012.6319840 |
| format | Conference Proceeding |
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L.</creatorcontrib><creatorcontrib>Sudderth, E. B.</creatorcontrib><title>Improved variational inference for tracking in clutter</title><title>2012 IEEE Statistical Signal Processing Workshop (SSP)</title><addtitle>SSP</addtitle><description>We apply the expectation propagation (EP) algorithm to temporally track targets using sensors that produce spurious clutter detections, and may sometimes fail to detect the true target. The variational inference framework underlying EP allows the tracker to be easily adapted to varying measurement models. We develop variants of EP based on single Gaussian and Gaussian mixture approximations of posterior target location distributions, which offer a tradeoff between accuracy and computational complexity. Experiments show improved tracking accuracy and uncertainty estimation relative to widely used baseline tracking algorithms.</description><subject>Approximation algorithms</subject><subject>Approximation methods</subject><subject>Bayesian inference</subject><subject>Clutter</subject><subject>Data models</subject><subject>expectation propagation</subject><subject>Hidden Markov models</subject><subject>Inference algorithms</subject><subject>Target tracking</subject><subject>variational methods</subject><issn>2373-0803</issn><issn>2693-3551</issn><isbn>9781467301824</isbn><isbn>1467301825</isbn><isbn>1467301817</isbn><isbn>9781467301817</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1kEtLw0AUhccXWGv2gpv8gdS5c-e5lOKjUFCorsvM5EZG06RMYsF_b8C6OnA--Dgcxm6ALwC4u9tsXheCg1hoBGclP2FXILVBDhbMKZsJ7bBCpeCMFc7Yfybk-cTQYMUtx0tWDMMn5xy0FWjFjOnVbp_7A9Xlwefkx9R3vi1T11CmLlLZ9Lkcs49fqfuY6jK23-NI-ZpdNL4dqDjmnL0_Prwtn6v1y9Nqeb-ukgAzVhi0dEqg54iu9iCdrClopaIKgZporVShNhE0n6YTWVtHbVRjZAwYpcY5u_3zJiLa7nPa-fyzPV6Av3IuSoQ</recordid><startdate>20120101</startdate><enddate>20120101</enddate><creator>Pacheco, J. 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B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i217t-3b649523a0339da1494deb655c5bbefc8845bd7c160355ee88dc675f74cb3c463</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Approximation algorithms</topic><topic>Approximation methods</topic><topic>Bayesian inference</topic><topic>Clutter</topic><topic>Data models</topic><topic>expectation propagation</topic><topic>Hidden Markov models</topic><topic>Inference algorithms</topic><topic>Target tracking</topic><topic>variational methods</topic><toplevel>online_resources</toplevel><creatorcontrib>Pacheco, J. L.</creatorcontrib><creatorcontrib>Sudderth, E. B.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Pacheco, J. L.</au><au>Sudderth, E. B.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Improved variational inference for tracking in clutter</atitle><btitle>2012 IEEE Statistical Signal Processing Workshop (SSP)</btitle><stitle>SSP</stitle><date>2012-01-01</date><risdate>2012</risdate><spage>852</spage><epage>855</epage><pages>852-855</pages><issn>2373-0803</issn><eissn>2693-3551</eissn><isbn>9781467301824</isbn><isbn>1467301825</isbn><eisbn>1467301817</eisbn><eisbn>9781467301817</eisbn><abstract>We apply the expectation propagation (EP) algorithm to temporally track targets using sensors that produce spurious clutter detections, and may sometimes fail to detect the true target. The variational inference framework underlying EP allows the tracker to be easily adapted to varying measurement models. We develop variants of EP based on single Gaussian and Gaussian mixture approximations of posterior target location distributions, which offer a tradeoff between accuracy and computational complexity. Experiments show improved tracking accuracy and uncertainty estimation relative to widely used baseline tracking algorithms.</abstract><pub>IEEE</pub><doi>10.1109/SSP.2012.6319840</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | 2012 IEEE Statistical Signal Processing Workshop (SSP), 2012, p.852-855 |
| issn | 2373-0803 2693-3551 |
| language | eng |
| recordid | cdi_ieee_primary_6319840 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Approximation algorithms Approximation methods Bayesian inference Clutter Data models expectation propagation Hidden Markov models Inference algorithms Target tracking variational methods |
| title | Improved variational inference for tracking in clutter |
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