Visual feature tracking based on PHD filter for vehicle detection

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Show simple item record García, Fernando Prioletti, Antonio Cerri, Pietro Broggi, Alberto Escalera Hueso, Arturo de la Armingol Moreno, José María 2016-10-03T10:57:13Z 2016-10-03T10:57:13Z 2014-10-07
dc.identifier.bibliographicCitation Information Fusion (FUSION), 2014 17th International Conference on. IEEE, pp. 1-6
dc.identifier.isbn 978-8-4901-2355-3
dc.description.abstract Vehicle detection is one of the classical application among the Advance Driver Assistance Systems (ADAS). Applications like emergency braking or adaptive cruise control (ACC) require accurate and reliable vehicle detection. In latest years the improvements in vision detection have lead to the introduction of computer vision to detect vehicles by means of these more economical sensors, with high reliability. In the present paper, a novel algorithm for vehicle detection and tracking based on a probability hypothesis density (PHD) filter is presented. The first detection is based on a fast machine learning algorithm (Adaboost) and Haar-Like features. Later, the tracking is performed, by means features detected within the bounding box provided by the vehicle detection. The features, are tracked by a PHD filter. The results of the features being tracked are combined together in the last step, based on several different methods. Test provided show the performance of the PHD filter in public sequences using the different methods proposed.
dc.description.sponsorship This work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03-01) and (GRANT TRA 2011-29454-C03-02).
dc.format.extent 7
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2014 IEEE
dc.subject.other PHD Filter
dc.subject.other Vehicle detection
dc.subject.other Computer vision
dc.subject.other Intelligent Transport Systems
dc.title Visual feature tracking based on PHD filter for vehicle detection
dc.type conferenceObject
dc.type bookPart
dc.subject.eciencia Robótica e Informática Industrial
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TRA2010-20225-C03-01
dc.relation.projectID Gobierno de España. TRA 2011-29454-C03-02
dc.relation.projectID Gobierno de España. TRA2013-48314-C3-1-R
dc.type.version acceptedVersion
dc.relation.eventdate 7-10 July 2014
dc.relation.eventplace Salamanca, Castilla y León, Spain
dc.relation.eventtitle 17th International Conference on Information Fusion
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 1
dc.identifier.publicationlastpage 6
dc.identifier.publicationtitle Information Fusion (FUSION), 2014 17th International Conference on
dc.identifier.uxxi CC/0000023263
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