GMM background modeling using divergence-based weight updating
Background modeling is a core task of video-based surveillance systems used to facilitate the online analysis of real-world scenes. Nowadays, GMM-based background modeling approaches are widely used, and several versions have been proposed to improve the original one proposed by Stauffer and Grimson...
- Autores:
-
Pulgarín Giraldo, Juan Diego
Castellanos Domínguez, German
Insuasti Ceballos, Hernan David
Álvarez Meza, Andrés Marino
Bouwmans, Thierry
- Tipo de recurso:
- Fecha de publicación:
- 2017
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/11223
- Acceso en línea:
- http://hdl.handle.net/10614/11223
- Palabra clave:
- Modelado
Modeling
Background modeling
GMM
Euclidean divergence
- Rights
- openAccess
- License
- Derechos Reservados - Universidad Autónoma de Occidente
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GMM background modeling using divergence-based weight updating |
| title |
GMM background modeling using divergence-based weight updating |
| spellingShingle |
GMM background modeling using divergence-based weight updating Modelado Modeling Background modeling GMM Euclidean divergence |
| title_short |
GMM background modeling using divergence-based weight updating |
| title_full |
GMM background modeling using divergence-based weight updating |
| title_fullStr |
GMM background modeling using divergence-based weight updating |
| title_full_unstemmed |
GMM background modeling using divergence-based weight updating |
| title_sort |
GMM background modeling using divergence-based weight updating |
| dc.creator.fl_str_mv |
Pulgarín Giraldo, Juan Diego Castellanos Domínguez, German Insuasti Ceballos, Hernan David Álvarez Meza, Andrés Marino Bouwmans, Thierry |
| dc.contributor.author.none.fl_str_mv |
Pulgarín Giraldo, Juan Diego Castellanos Domínguez, German Insuasti Ceballos, Hernan David Álvarez Meza, Andrés Marino Bouwmans, Thierry |
| dc.subject.lemb.spa.fl_str_mv |
Modelado |
| topic |
Modelado Modeling Background modeling GMM Euclidean divergence |
| dc.subject.lemb.eng.fl_str_mv |
Modeling |
| dc.subject.proposal.eng.fl_str_mv |
Background modeling GMM Euclidean divergence |
| description |
Background modeling is a core task of video-based surveillance systems used to facilitate the online analysis of real-world scenes. Nowadays, GMM-based background modeling approaches are widely used, and several versions have been proposed to improve the original one proposed by Stauffer and Grimson. Nonetheless, the cost function employed to update the GMM weight parameters has not received major changes and is still set by means of a single binary reference, which mostly leads to noisy foreground masks when the ownership of a pixel to the background model is uncertain. To cope with this issue, we propose a cost function based on Euclidean divergence, providing nonlinear smoothness to the background modeling process. Achieved results over well-known datasets show that the proposed cost function supports the foreground/background discrimination, reducing the number of false positives, especially, in highly dynamical scenarios |
| publishDate |
2017 |
| dc.date.issued.none.fl_str_mv |
2017-02-16 |
| dc.date.accessioned.none.fl_str_mv |
2019-10-17T13:19:33Z |
| dc.date.available.none.fl_str_mv |
2019-10-17T13:19:33Z |
| dc.type.spa.fl_str_mv |
Capítulo - Parte de Libro |
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http://purl.org/coar/resource_type/c_3248 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Text |
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info:eu-repo/semantics/publishedVersion |
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9783319597393 |
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9783319522777 |
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http://hdl.handle.net/10614/11223 |
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9783319597393 9783319522777 |
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http://hdl.handle.net/10614/11223 |
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eng |
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eng |
| dc.relation.spa.fl_str_mv |
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications : 21st Iberoamerican Congress, CIARP 2016, Lima, Peru, November 8–11, 2016, Proceedings. Páginas 282-290 |
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290 290 |
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282 282 |
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Pulgarin-Giraldo, J. D., Alvarez-Meza, A., Insuasti-Ceballos, D., Bouwmans, T., & Castellanos-Dominguez, G. (2016, November). GMM Background Modeling Using Divergence-Based Weight Updating. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2016. Lecture Notes in Computer Science(), vol 10125. Springer, Cham. https://doi.org/10.1007/978-3-319-52277-7_35 |
| dc.relation.ispartofbook.eng.fl_str_mv |
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications |
| dc.relation.references.none.fl_str_mv |
1. Molina-Giraldo, S., ´Alvarez-Meza, A.M., Garc´ıa-´Alvarez, J.C., Castellanos-Dom´ınguez, C.G.: Video segmentation framework by dynamic background modelling. In: Petrosino, A. (ed.) ICIAP 2013. LNCS, vol. 8156, pp. 843–852. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41181-6 85 2. Maddalena, L., Petrosino, A.: A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans. Image Process. 17(7), 1168–1177 (2008) 3. Alvarez-Meza, A.M., Molina-Giraldo, S., Castellanos-Dominguez, G.: Background modeling using object-based selective updating and correntropy adaptation. Image Vis. Comput. 45, 22–36 (2016) 4. Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2. IEEE (1999) 5. Bouwmans, T., El Baf, F., Vachon, B., et al.: Background modeling using mixture of Gaussians for foreground detection - a survey. Recent Pat. Comput. Sci. 1(3), 219–237 (2008) 6. Hayman, E., Eklundh, J.-O.: Statistical background subtraction for a mobile observer. In: Proceedings of Ninth IEEE International Conference on Computer Vision, vol. 1, pp. 67–74, October 2003 7. Principe, J.: Information Theoretic Learning. Renyi’s Entropy and Kernel Perspectives. Springer, New York (2010) 8. Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 2, pp. 28–31, August 2004 9. Sobral, A.: BGSLibrary: an OpenCV C++ background subtraction library. In: IX Workshop de Vis˜ao Computacional (WVC 2013), Rio de Janeiro, Brazil, pp. 38–43, June 2013 |
| dc.rights.spa.fl_str_mv |
Derechos Reservados - Universidad Autónoma de Occidente |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) |
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Derechos Reservados - Universidad Autónoma de Occidente https://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) http://purl.org/coar/access_right/c_abf2 |
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Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí |
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Springer, Cham |
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Pulgarín Giraldo, Juan Diegovirtual::4144-1Castellanos Domínguez, German3de8cb9245317beddda7d5f7dade0b1bInsuasti Ceballos, Hernan David7e6a0fd1b91e6d3130037902506a9469Álvarez Meza, Andrés Marino7fd52c5e946073a9aac3ed6f493759d7Bouwmans, Thierry9b6333ed1f9e5a14cb40f8a4e7e78215Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí2019-10-17T13:19:33Z2019-10-17T13:19:33Z2017-02-1697833195973939783319522777http://hdl.handle.net/10614/11223Background modeling is a core task of video-based surveillance systems used to facilitate the online analysis of real-world scenes. Nowadays, GMM-based background modeling approaches are widely used, and several versions have been proposed to improve the original one proposed by Stauffer and Grimson. Nonetheless, the cost function employed to update the GMM weight parameters has not received major changes and is still set by means of a single binary reference, which mostly leads to noisy foreground masks when the ownership of a pixel to the background model is uncertain. To cope with this issue, we propose a cost function based on Euclidean divergence, providing nonlinear smoothness to the background modeling process. Achieved results over well-known datasets show that the proposed cost function supports the foreground/background discrimination, reducing the number of false positives, especially, in highly dynamical scenariosapplication/pdf9 páginasengSpringer, ChamProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications : 21st Iberoamerican Congress, CIARP 2016, Lima, Peru, November 8–11, 2016, Proceedings. Páginas 282-290290290282282Pulgarin-Giraldo, J. D., Alvarez-Meza, A., Insuasti-Ceballos, D., Bouwmans, T., & Castellanos-Dominguez, G. (2016, November). GMM Background Modeling Using Divergence-Based Weight Updating. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2016. Lecture Notes in Computer Science(), vol 10125. Springer, Cham. https://doi.org/10.1007/978-3-319-52277-7_35Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications1. Molina-Giraldo, S., ´Alvarez-Meza, A.M., Garc´ıa-´Alvarez, J.C., Castellanos-Dom´ınguez, C.G.: Video segmentation framework by dynamic background modelling. In: Petrosino, A. (ed.) ICIAP 2013. LNCS, vol. 8156, pp. 843–852. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41181-6 852. Maddalena, L., Petrosino, A.: A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans. Image Process. 17(7), 1168–1177 (2008)3. Alvarez-Meza, A.M., Molina-Giraldo, S., Castellanos-Dominguez, G.: Background modeling using object-based selective updating and correntropy adaptation. Image Vis. Comput. 45, 22–36 (2016)4. Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2. IEEE (1999)5. Bouwmans, T., El Baf, F., Vachon, B., et al.: Background modeling using mixture of Gaussians for foreground detection - a survey. Recent Pat. Comput. Sci. 1(3), 219–237 (2008)6. Hayman, E., Eklundh, J.-O.: Statistical background subtraction for a mobile observer. In: Proceedings of Ninth IEEE International Conference on Computer Vision, vol. 1, pp. 67–74, October 20037. Principe, J.: Information Theoretic Learning. Renyi’s Entropy and Kernel Perspectives. Springer, New York (2010)8. Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 2, pp. 28–31, August 20049. Sobral, A.: BGSLibrary: an OpenCV C++ background subtraction library. In: IX Workshop de Vis˜ao Computacional (WVC 2013), Rio de Janeiro, Brazil, pp. 38–43, June 2013Derechos Reservados - Universidad Autónoma de Occidentehttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2https://link.springer.com/chapter/10.1007/978-3-319-52277-7_35GMM background modeling using divergence-based weight updatingCapítulo - Parte de Librohttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85ModeladoModelingBackground modelingGMMEuclidean divergencePublication33e9b6b4-bd6d-4b86-b500-ae237e1e9a98virtual::4144-133e9b6b4-bd6d-4b86-b500-ae237e1e9a98virtual::4144-1https://scholar.google.com.co/citations?user=Bwuc2BkAAAAJ&hl=envirtual::4144-10000-0002-6409-5104virtual::4144-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000207497virtual::4144-1CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://red.uao.edu.co/bitstreams/c40ae1cf-cef6-443d-b41e-247380592475/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://red.uao.edu.co/bitstreams/db99b8e3-dbfe-4e93-af3d-f80815ec150a/download20b5ba22b1117f71589c7318baa2c560MD53ORIGINALGMM background modeling using divergence-based weight updating.pdfGMM background modeling using divergence-based weight updating.pdfTexto archivo completo del artículo de revista, PDFapplication/pdf778313https://red.uao.edu.co/bitstreams/e68a39ed-83ef-4425-89f9-1f2f34783d94/download9389bbf7245582858e67b9b85f12a473MD54TEXTGMM background modeling using divergence-based weight updating.pdf.txtGMM background modeling using divergence-based weight updating.pdf.txtExtracted texttext/plain21011https://red.uao.edu.co/bitstreams/a90f320e-710c-4408-a8f9-26da29cd28f7/downloadb8c84f5ac596662bbb5ec0f01570ba36MD55THUMBNAILGMM background modeling using divergence-based weight updating.pdf.jpgGMM background modeling using divergence-based weight updating.pdf.jpgGenerated Thumbnailimage/jpeg11783https://red.uao.edu.co/bitstreams/3aef4060-a16f-450a-bf00-85076f981de9/downloadbe0b8161b1fcacb8355d877290fba21cMD5610614/11223oai:red.uao.edu.co:10614/112232024-03-13 14:15:26.539https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos Reservados - Universidad Autónoma de Occidenteopen.accesshttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.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 |