Monte-Carlo-Based Parametric Motion Estimation Using a Hybrid Model Approach
Parametric motion estimation is an important task for various video processing applications, such as analysis, segmentation, and coding. The process for such an estimation has to satisfy three requirements. It has to be fast, accurate, and robust in the presence of arbitrarily moving foreground obje...
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| Veröffentlicht in: | IEEE transactions on circuits and systems for video technology 2013-04, Vol.23 (4), p.607-620 |
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| container_title | IEEE transactions on circuits and systems for video technology |
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| creator | Tok, M. Glantz, A. Krutz, A. Sikora, T. |
| description | Parametric motion estimation is an important task for various video processing applications, such as analysis, segmentation, and coding. The process for such an estimation has to satisfy three requirements. It has to be fast, accurate, and robust in the presence of arbitrarily moving foreground objects. We introduce a two-step simplification scheme, suitable for Monte-Carlo-based perspective motion model estimation. For complexity reduction, the Helmholtz tradeoff estimator as well as random sample consensus are enhanced with this scheme and applied on Kanade-Lucas-Tomasi features as well as on video stream macroblock motion vector fields. For the feature-based estimation, good trackable features are detected and tracked on raw video sequences. For the block-based approach, motion vector fields from encoded H.264/AVC video streams are used. Results indicate that the complexity of the whole estimation process can be reduced by a factor of up to 10000 compared to state-of-the-art methods without losing estimation precision. |
| doi_str_mv | 10.1109/TCSVT.2012.2211173 |
| format | Article |
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The process for such an estimation has to satisfy three requirements. It has to be fast, accurate, and robust in the presence of arbitrarily moving foreground objects. We introduce a two-step simplification scheme, suitable for Monte-Carlo-based perspective motion model estimation. For complexity reduction, the Helmholtz tradeoff estimator as well as random sample consensus are enhanced with this scheme and applied on Kanade-Lucas-Tomasi features as well as on video stream macroblock motion vector fields. For the feature-based estimation, good trackable features are detected and tracked on raw video sequences. For the block-based approach, motion vector fields from encoded H.264/AVC video streams are used. 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(IEEE) Apr 2013</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c358t-4a04774b8fa4c079dda298d1906bacfdbb0d47ed780234e764fd134dfb9aea063</citedby><cites>FETCH-LOGICAL-c358t-4a04774b8fa4c079dda298d1906bacfdbb0d47ed780234e764fd134dfb9aea063</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6257457$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,778,782,794,27911,27912,54745</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6257457$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27283268$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Tok, M.</creatorcontrib><creatorcontrib>Glantz, A.</creatorcontrib><creatorcontrib>Krutz, A.</creatorcontrib><creatorcontrib>Sikora, T.</creatorcontrib><title>Monte-Carlo-Based Parametric Motion Estimation Using a Hybrid Model Approach</title><title>IEEE transactions on circuits and systems for video technology</title><addtitle>TCSVT</addtitle><description>Parametric motion estimation is an important task for various video processing applications, such as analysis, segmentation, and coding. The process for such an estimation has to satisfy three requirements. It has to be fast, accurate, and robust in the presence of arbitrarily moving foreground objects. We introduce a two-step simplification scheme, suitable for Monte-Carlo-based perspective motion model estimation. For complexity reduction, the Helmholtz tradeoff estimator as well as random sample consensus are enhanced with this scheme and applied on Kanade-Lucas-Tomasi features as well as on video stream macroblock motion vector fields. For the feature-based estimation, good trackable features are detected and tracked on raw video sequences. For the block-based approach, motion vector fields from encoded H.264/AVC video streams are used. Results indicate that the complexity of the whole estimation process can be reduced by a factor of up to 10000 compared to state-of-the-art methods without losing estimation precision.</description><subject>Applied sciences</subject><subject>Complexity</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Estimation</subject><subject>Exact sciences and technology</subject><subject>Global motion model</subject><subject>Helmholtz tradeoff estimator</subject><subject>Image processing</subject><subject>Information, signal and communications theory</subject><subject>Mathematical analysis</subject><subject>Mathematical model</subject><subject>Monte Carlo methods</subject><subject>Monte-Carlo method</subject><subject>Motion estimation</subject><subject>Motion simulation</subject><subject>parametric motion estimation</subject><subject>robust regression</subject><subject>Robustness</subject><subject>Segmentation</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal, noise</subject><subject>Sprites (computer)</subject><subject>Streams</subject><subject>Studies</subject><subject>Telecommunications and information theory</subject><subject>Vectors</subject><subject>Vectors (mathematics)</subject><subject>Video</subject><issn>1051-8215</issn><issn>1558-2205</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE9PwzAMxSsEEmPwBeBSCSFx6YjTpEmPYxoMaRNIbFwrN0mhU9eMpDvs25P90Q6c_CT__Gy_KLoFMgAg-dN89Pk1H1ACdEApAIj0LOoB5zKhlPDzoAmHRFLgl9GV90tCgEkmetF0ZtvOJCN0jU2e0Rsdf6DDlelcreKZ7WrbxmPf1Svcy4Wv2-8Y48m2dLUOgDZNPFyvnUX1cx1dVNh4c3Os_WjxMp6PJsn0_fVtNJwmKuWySxgSJgQrZYVMEZFrjTSXGnKSlagqXZZEM2G0kISmzIiMVRpSpqsyR4MkS_vR48E3rP3dGN8Vq9or0zTYGrvxBaQZBypolgf0_h-6tBvXhusCRTlnkGc7Q3qglLPeO1MVaxc-dtsCSLELuNgHXOwCLo4Bh6GHozV6hU3lsFW1P02G_TKlmQzc3YGrjTGndka5YFykf5P_gxc</recordid><startdate>20130401</startdate><enddate>20130401</enddate><creator>Tok, M.</creator><creator>Glantz, A.</creator><creator>Krutz, A.</creator><creator>Sikora, T.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Applied sciences Complexity Detection, estimation, filtering, equalization, prediction Estimation Exact sciences and technology Global motion model Helmholtz tradeoff estimator Image processing Information, signal and communications theory Mathematical analysis Mathematical model Monte Carlo methods Monte-Carlo method Motion estimation Motion simulation parametric motion estimation robust regression Robustness Segmentation Signal and communications theory Signal processing Signal, noise Sprites (computer) Streams Studies Telecommunications and information theory Vectors Vectors (mathematics) Video |
| title | Monte-Carlo-Based Parametric Motion Estimation Using a Hybrid Model Approach |
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