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Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Video analysis-based vehicle detection and tracking using an MCMC sampling framework | EURASIP Journal on Advances in Signal Processing SpringerOpen0 This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text HTML versions will be made available soon. Video analysis-based vehicle detection and tracking using an MCMC sampling framework EURASIP Journal on Advances in Signal Processing 2012 2012 2 doi 10.1186 1687-6180-2012-2 Jon Arrospide jal@gti.ssr.upm.es Luis Salgado lsa@gti.ssr.upm.es Marcos Nieto mnieto@vicomtech.org ISSN 1687-6180 Article type Research Submission date 15 May 2011 Acceptance date 6 January 2012 Publication date 6 January 2012 Article URL http asp.eurasipiournals.com content 2012 1 2 This peer-reviewed article was published immediately upon acceptance. It can be downloaded printed and distributed freely for any purposes see copyright notice below . For information about publishing your research in EURASIP Journal on Advances in Signal Processing go to http asp.eurasipjournals.com authors instructions For information about other SpringerOpen publications go to http www.springeropen.com 2012 Arrospide etal. licensee Springer. This is an open access article distributed under the terms of the Creative Commons Attribution License http creativecommons.org licenses by 2.0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Video analysis-based vehicle detection and tracking using an MCMC sampling framework Jon Arróspide 1 Luis Salgado1 and Marcos Nieto2 1Escuela Tácnica Superior de Ingenieros de Telecomunicacián Universidad Politácnica de Madrid Grupo de Tratamiento de Imágenes Madrid 28040 Spain 2Vicomtech-IK4 Research Alliance San Sebastián 20009 Spain Corresponding author jal@gti.ssr.upm.es Email addresses LS lsa@gti.ssr.upm.es MN mnieto@vicomtech.org Abstract This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted .