Data Association Based Tracking Traffic Objects
For the widely demanding of adaptive multiple moving objects tracking in intelligent transportation field, a new type of traffic video based multi-object tracking method is presented. Background is modeled by difference of Gaussians (DOG) probability kernel and background subtraction is used to dete...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gao, Tao [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Umfang: |
1 Online-Ressource |
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| Übergeordnetes Werk: |
Enthalten in: International journal of advanced pervasive and ubiquitous computing - Hershey, Pa : IGI Global, 2009, 5(2013), 2, Seite 31-46 |
|---|---|
| Übergeordnetes Werk: |
volume:5 ; year:2013 ; number:2 ; pages:31-46 |
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| DOI / URN: |
10.4018/japuc.2013040104 |
|---|
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NLEJ251787168 |
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| 520 | |a For the widely demanding of adaptive multiple moving objects tracking in intelligent transportation field, a new type of traffic video based multi-object tracking method is presented. Background is modeled by difference of Gaussians (DOG) probability kernel and background subtraction is used to detect multiple moving objects. After obtaining the foreground, shadow is eliminated by an edge detection method. A type of particle filtering combined with SIFT method is used for motion tracking. A queue chain method is used to record data association among different objects, which could improve the detection accuracy and reduce the complexity. By actual road tests, the system tracks multi-object with a better performance of real time and mutual occlusion robustness, indicating that it is effective for intelligent transportation system | ||
| 653 | |a Intelligent Transportation System |a Moving Objects Tracking |a Motion Detection |a Particle Filtering |a Scale Invariant Feature Transform (SIFT) | ||
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10.4018/japuc.2013040104 doi (DE-627)NLEJ251787168 (VZGNL)10.4018/japuc.2013040104 DE-627 ger DE-627 rakwb eng Gao, Tao verfasserin aut Data Association Based Tracking Traffic Objects 2013 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For the widely demanding of adaptive multiple moving objects tracking in intelligent transportation field, a new type of traffic video based multi-object tracking method is presented. Background is modeled by difference of Gaussians (DOG) probability kernel and background subtraction is used to detect multiple moving objects. After obtaining the foreground, shadow is eliminated by an edge detection method. A type of particle filtering combined with SIFT method is used for motion tracking. A queue chain method is used to record data association among different objects, which could improve the detection accuracy and reduce the complexity. By actual road tests, the system tracks multi-object with a better performance of real time and mutual occlusion robustness, indicating that it is effective for intelligent transportation system Intelligent Transportation System Moving Objects Tracking Motion Detection Particle Filtering Scale Invariant Feature Transform (SIFT) Enthalten in International journal of advanced pervasive and ubiquitous computing Hershey, Pa : IGI Global, 2009 5(2013), 2, Seite 31-46 Online-Ressource (DE-627)NLEJ244418632 (DE-600)2695914-8 1937-9668 nnns volume:5 year:2013 number:2 pages:31-46 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/japuc.2013040104 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/japuc.2013040104&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 5 2013 2 31-46 |
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10.4018/japuc.2013040104 doi (DE-627)NLEJ251787168 (VZGNL)10.4018/japuc.2013040104 DE-627 ger DE-627 rakwb eng Gao, Tao verfasserin aut Data Association Based Tracking Traffic Objects 2013 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For the widely demanding of adaptive multiple moving objects tracking in intelligent transportation field, a new type of traffic video based multi-object tracking method is presented. Background is modeled by difference of Gaussians (DOG) probability kernel and background subtraction is used to detect multiple moving objects. After obtaining the foreground, shadow is eliminated by an edge detection method. A type of particle filtering combined with SIFT method is used for motion tracking. A queue chain method is used to record data association among different objects, which could improve the detection accuracy and reduce the complexity. By actual road tests, the system tracks multi-object with a better performance of real time and mutual occlusion robustness, indicating that it is effective for intelligent transportation system Intelligent Transportation System Moving Objects Tracking Motion Detection Particle Filtering Scale Invariant Feature Transform (SIFT) Enthalten in International journal of advanced pervasive and ubiquitous computing Hershey, Pa : IGI Global, 2009 5(2013), 2, Seite 31-46 Online-Ressource (DE-627)NLEJ244418632 (DE-600)2695914-8 1937-9668 nnns volume:5 year:2013 number:2 pages:31-46 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/japuc.2013040104 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/japuc.2013040104&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 5 2013 2 31-46 |
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For the widely demanding of adaptive multiple moving objects tracking in intelligent transportation field, a new type of traffic video based multi-object tracking method is presented. Background is modeled by difference of Gaussians (DOG) probability kernel and background subtraction is used to detect multiple moving objects. After obtaining the foreground, shadow is eliminated by an edge detection method. A type of particle filtering combined with SIFT method is used for motion tracking. A queue chain method is used to record data association among different objects, which could improve the detection accuracy and reduce the complexity. By actual road tests, the system tracks multi-object with a better performance of real time and mutual occlusion robustness, indicating that it is effective for intelligent transportation system |
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For the widely demanding of adaptive multiple moving objects tracking in intelligent transportation field, a new type of traffic video based multi-object tracking method is presented. Background is modeled by difference of Gaussians (DOG) probability kernel and background subtraction is used to detect multiple moving objects. After obtaining the foreground, shadow is eliminated by an edge detection method. A type of particle filtering combined with SIFT method is used for motion tracking. A queue chain method is used to record data association among different objects, which could improve the detection accuracy and reduce the complexity. By actual road tests, the system tracks multi-object with a better performance of real time and mutual occlusion robustness, indicating that it is effective for intelligent transportation system |
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For the widely demanding of adaptive multiple moving objects tracking in intelligent transportation field, a new type of traffic video based multi-object tracking method is presented. Background is modeled by difference of Gaussians (DOG) probability kernel and background subtraction is used to detect multiple moving objects. After obtaining the foreground, shadow is eliminated by an edge detection method. A type of particle filtering combined with SIFT method is used for motion tracking. A queue chain method is used to record data association among different objects, which could improve the detection accuracy and reduce the complexity. By actual road tests, the system tracks multi-object with a better performance of real time and mutual occlusion robustness, indicating that it is effective for intelligent transportation system |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ251787168</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231205143829.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231128s2013 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.4018/japuc.2013040104</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ251787168</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(VZGNL)10.4018/japuc.2013040104</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Gao, Tao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data Association Based Tracking Traffic Objects</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2013</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">For the widely demanding of adaptive multiple moving objects tracking in intelligent transportation field, a new type of traffic video based multi-object tracking method is presented. Background is modeled by difference of Gaussians (DOG) probability kernel and background subtraction is used to detect multiple moving objects. After obtaining the foreground, shadow is eliminated by an edge detection method. A type of particle filtering combined with SIFT method is used for motion tracking. A queue chain method is used to record data association among different objects, which could improve the detection accuracy and reduce the complexity. By actual road tests, the system tracks multi-object with a better performance of real time and mutual occlusion robustness, indicating that it is effective for intelligent transportation system</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Intelligent Transportation System</subfield><subfield code="a">Moving Objects Tracking</subfield><subfield code="a">Motion Detection</subfield><subfield code="a">Particle Filtering</subfield><subfield code="a">Scale Invariant Feature Transform (SIFT)</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of advanced pervasive and ubiquitous computing</subfield><subfield code="d">Hershey, Pa : IGI Global, 2009</subfield><subfield code="g">5(2013), 2, Seite 31-46</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ244418632</subfield><subfield code="w">(DE-600)2695914-8</subfield><subfield code="x">1937-9668</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:5</subfield><subfield code="g">year:2013</subfield><subfield code="g">number:2</subfield><subfield code="g">pages:31-46</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/japuc.2013040104</subfield><subfield code="m">X:IGIG</subfield><subfield code="x">Verlag</subfield><subfield code="z">Deutschlandweit zugänglich</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/japuc.2013040104&buylink=true</subfield><subfield code="3">Abstract</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-GIS</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">5</subfield><subfield code="j">2013</subfield><subfield code="e">2</subfield><subfield code="h">31-46</subfield></datafield></record></collection>
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