Stochastic visual tracking with active appearance models

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dc.contributor.advisorHerbst, B. M.en_ZA
dc.contributor.advisorHunter, K.en_ZA
dc.contributor.advisorVan Zijl, L.en_ZA
dc.contributor.advisorDu Preez, J.en_ZA
dc.contributor.authorHoffmann, McElory Robertoen_ZA
dc.contributor.otherUniversity of Stellenbosch. Faculty of Engineering. Dept. of Mathematical Sciences. Applied Mathematics.
dc.date.accessioned2009-11-17T13:55:56Zen_ZA
dc.date.accessioned2010-06-01T08:20:08Z
dc.date.available2009-11-17T13:55:56Zen_ZA
dc.date.available2010-06-01T08:20:08Z
dc.date.issued2009-12
dc.descriptionThesis (PhD (Applied Mathematics))--University of Stellenbosch, 2009.en_ZA
dc.description.abstractENGLISH ABSTRACT: In many applications, an accurate, robust and fast tracker is needed, for example in surveillance, gesture recognition, tracking lips for lip-reading and creating an augmented reality by embedding a tracked object in a virtual environment. In this dissertation we investigate the viability of a tracker that combines the accuracy of active appearancemodels with the robustness of the particle lter (a stochastic process)—we call this combination the PFAAM. In order to obtain a fast system, we suggest local optimisation as well as using active appearance models tted with non-linear approaches. Active appearance models use both contour (shape) and greyscale information to build a deformable template of an object. ey are typically accurate, but not necessarily robust, when tracking contours. A particle lter is a generalisation of the Kalman lter. In a tutorial style, we show how the particle lter is derived as a numerical approximation for the general state estimation problem. e algorithms are tested for accuracy, robustness and speed on a PC, in an embedded environment and by tracking in ìD. e algorithms run real-time on a PC and near real-time in our embedded environment. In both cases, good accuracy and robustness is achieved, even if the tracked object moves fast against a cluttered background, and for uncomplicated occlusions.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: ’nAkkurate, robuuste en vinnige visuele-opspoorderword in vele toepassings benodig. Voorbeelde van toepassings is bewaking, gebaarherkenning, die volg van lippe vir liplees en die skep van ’n vergrote realiteit deur ’n voorwerp wat gevolg word, in ’n virtuele omgewing in te bed. In hierdie proefskrif ondersoek ons die lewensvatbaarheid van ’n visuele-opspoorder deur die akkuraatheid van aktiewe voorkomsmodellemet die robuustheid van die partikel lter (’n stochastiese proses) te kombineer—ons noem hierdie kombinasie die PFAAM. Ten einde ’n vinnige visuele-opspoorder te verkry, stel ons lokale optimering, sowel as die gebruik van aktiewe voorkomsmodelle wat met nie-lineêre tegnieke gepas is, voor. Aktiewe voorkomsmodelle gebruik kontoer (vorm) inligting tesamemet grysskaalinligting om ’n vervormbaremeester van ’n voorwerp te bou. Wanneer aktiewe voorkomsmodelle kontoere volg, is dit normaalweg akkuraat,maar nie noodwendig robuust nie. ’n Partikel lter is ’n veralgemening van die Kalman lter. Ons wys in tutoriaalstyl hoe die partikel lter as ’n numeriese benadering tot die toestand-beramingsprobleem afgelei kan word. Die algoritmes word vir akkuraatheid, robuustheid en spoed op ’n persoonlike rekenaar, ’n ingebedde omgewing en deur volging in ìD, getoets. Die algoritmes loop intyds op ’n persoonlike rekenaar en is naby intyds op ons ingebedde omgewing. In beide gevalle, word goeie akkuraatheid en robuustheid verkry, selfs as die voorwerp wat gevolg word, vinnig, teen ’n besige agtergrond beweeg of eenvoudige okklusies ondergaan.en_ZA
dc.identifier.urihttp://hdl.handle.net/10019.1/1381
dc.language.isoenen_ZA
dc.publisherStellenbosch : University of Stellenbosch
dc.rights.holderUniversity of Stellenbosch
dc.subjectActive appearance modelsen_ZA
dc.subjectParticle filtersen_ZA
dc.subjectTrackingen_ZA
dc.subjectDissertations -- Applied mathematicsen
dc.subjectTheses -- Applied mathematicsen
dc.subject.lcshStochastic modelsen_ZA
dc.subject.lcshComputer visionen_ZA
dc.titleStochastic visual tracking with active appearance modelsen_ZA
dc.typeThesisen_ZA
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