Stochastic visual tracking with active appearance models

Thesis (PhD (Applied Mathematics))--University of Stellenbosch, 2009.ENGLISH 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.AFRIKAANSE 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

The hitchhiker's guide to the particle filter

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Realtime LEGO Brick Image Retrieval with Cellular Automata

We consider the realtime content-based image retrieval of LEGO bricks from a database of images of LEGO bricks. This seemingly simple problem contains a number of surprisingly the image signature, and corresponding feature set, and illustrate cellular automaton-based methods for the whole feature extraction phase

Occlusions and active appearance models

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Opening doors: An initial SRL approach

Opening doors is an essential task that a robot should perform. In this paper, we propose an approach to predict the action of opening doors, together with the action point where the action should be performed. The input of the classifier is a set of bounding boxes of the door and door handle, together with background knowledge in the form of logical rules. Learning and inference are performed with the probabilistic programming language ProbLog. We evaluate our approach on a dataset of doors and encouraging results are obtained.status: publishe

On visual object tracking using active appearance models

Active appearance models provide an elegant framework for tracking objects. Using them in a deterministic algorithm to perforin tracking is not robust enough since no history is used of the object's movement and position. We discuss two approaches to rectify this situation. Both techniques are based on the particle filter. The first technique initialises the active appearance model search algorithm with a shape estimate obtained from an active contour tracker. A combination of a particle filter and an active appearance model forms the foundation for the second technique. Experimental results indicate the effectiveness of these techniques.Articl

Realtime LEGO Brick Image Retrieval with Cellular Automata

We consider the realtime content-based image retrieval of LEGO bricks from a database of images of LEGO bricks. This seemingly simple problem contains a number of surprisingly the image signature, and corresponding feature set, and illustrate cellular automaton-based methods for the whole feature extraction phase

Realtime LEGO Brick Image Retrieval with Cellular Automata

We consider the realtime content-based image retrieval of LEGO bricks from a database of images of LEGO bricks. This seemingly simple problem contains a number of surprisingly the image signature, and corresponding feature set, and illustrate cellular automaton-based methods for the whole feature extraction phase

PFAAM - An active appearance model based particle filter for both robust and precise tracking

Please help us populate SUNScholar with the post print version of this article. It can be e-mailed to: [email protected] Wiskund

Realtime LEGO Brick Image Retrieval with Cellular Automata

Abstract: We consider the realtime content-based image retrieval of LEGO bricks from a database of images of LEGO bricks. This seemingly simple problem contains a number of surprisingly the image signature, and corresponding feature set, and illustrate cellular automaton-based methods for the whole feature extraction phase. Key Words: Content-based image retrieva