Báo cáo sinh học: An advanced Bayesian model for the visual tracking of multiple interacting objects

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: An advanced Bayesian model for the visual tracking of multiple interacting objects
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Báo cáo sinh học: " An advanced Bayesian model for the visual tracking of multiple interacting objects"EURASIP Journal on Advancesin Signal Processing 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. An advanced Bayesian model for the visual tracking of multiple interacting objects EURASIP Journal on Advances in Signal Processing 2011, 2011:130 doi:10.1186/1687-6180-2011-130 Carlos R del Blanco (cda@gti.ssr.upm.es) Fernando Jaureguizar (fjn@gti.ssr.upm.es) Narciso Garcia (narciso@gti.ssr.upm.es) ISSN 1687-6180 Article type Research Submission date 14 May 2011 Acceptance date 12 December 2011 Publication date 12 December 2011 Article URL http://asp.eurasipjournals.com/content/2011/1/130 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 © 2011 del Blanco et al. ; 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.An advanced Bayesian model for the visualtracking of multiple interacting objectsCarlos R del Blanco∗ , Fernando Jaureguizar and Narciso Garc´ ıaEscuela T´cnica Superior de Ingenieros de Telecomunicaci´n, e oUniversidad Polit´cnica de Madrid, Madrid, 28040, Spain e∗ Corresponding author: cda@gti.ssr.upm.esEmail addresses:FJ: fjn@gti.ssr.upm.esNG: narciso@gti.ssr.upm.esWebsite address:http://www.gti.ssr.upm.esAbstract Visual tracking of multiple objects is a key component of manyvisual-based systems. While there are reliable algorithms for tracking a sin-gle object in constrained scenarios, the object tracking is still a challengein uncontrolled situations involving multiple interacting objects that havea complex dynamics. In this article, a novel Bayesian model for trackingmultiple interacting objects in unrestricted situations is proposed. This isaccomplished by means of an advanced object dynamic model that pre-dicts possible interactive behaviors, which in turn depend on the inferenceof potential events of object occlusion. The proposed tracking model canalso handle false and missing detections that are typical from visual objectdetectors operating in uncontrolled scenarios. On the other hand, a Rao–2Blackwellization technique has been used to improve the accuracy of theestimated object trajectories, which is a fundamental aspect in the trackingof multiple objects due to its high dimensionality. Excellent results havebeen obtained using a publicly available database, proving the efficiency ofthe proposed approach.Keywords: visual tracking; multiple objects; interacting model; particlefilter; Rao–Blackwellization; data association.1 IntroductionVisual object tracking is a fundamental part in many video-based systemssuch as vehicle navigation, traffic monitoring, human–computer interaction,motion-based recognition, security and surveillance, etc. While there existreliable algorithms for the tracking of a single object in constrained sce-narios, the object tracking is still a challenge in uncontrolled situationsinvolving multiple objects with complex dynamics. The main problem isthat object detectors produce a set of unlabeled and unordered detections,whose correspondence with the tracked objects is unknown. The estimationof this correspondence, called the data association problem, is of paramountimportance for the proper estimation of the object trajectories. In addition,visual object detectors can produce false and ...