Reference : Real-time Simultaneous Modelling and Tracking of Articulated Objects
Dissertations and theses : Doctoral thesis
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/130961
Real-time Simultaneous Modelling and Tracking of Articulated Objects
English
Declercq, Arnaud mailto [Université de Liège - ULg > > > Doct. sc. ingé.(élec.& électro. - Bologne)]
2012
University of Liege, ​​Belgium
Doctor of Philosophy
Verly, Jacques mailto
Piater, Justus
Van Droogenbroeck, Marc mailto
Wehenkel, Louis mailto
Sebe, Nicu
[en] computer vision ; learning ; tracking
[en] In terms of capability, there is still a huge gap between the human visual system
and existing computer vision algorithms. To achieve results of su cient quality,
these algorithms are generally extremely specialised in the task they have been
designed for. All the knowledge available during their implementation is used
to bias the output result and/or facilitate the initialisation of the system. This
leads to increased robustness but a lower reusability of the code. In most cases,
it also majorly limits the freedom of the user by constraining him to a limited
set of possible interactions.
In this thesis, we propose to go in the opposite direction by developing a
general framework capable of both tracking and learning objects as complex
as articulated objects. The robustness will be achieved by using one task to
assist the other. The method should be completely unsupervised with no prior
knowledge about the appearance or shape of the objects encountered (although,
we decided to focus on rigid and articulated objects). With this framework,
we hope to provide directions for a more di cult and distant goal: that of
completely eliminating the time consuming prior design of object models in
computer vision applications. This long term target will allow the reduction
of the time and cost of implementing computer vision applications. It will also
provide a larger freedom in the range of objects that can be used by the program.
Our research focuses on three main aspects of this framework. The rst one is
to create an object description e ective on a wide variety of complex objects and
able to assist the object tracking while being learnt. The second is to provide
both tracking and learning methods that can be executed simultaneously in
real-time. This is particularly challenging for tracking when a large number of
features are involved. Finally, our most challenging task and the core of this
thesis, is to design robust tracking and learning solutions able to assist each
other without creating counter-productive bias when one of them fails.
Fonds pour la formation à la Recherche dans l'Industrie et dans l'Agriculture (Communauté française de Belgique) - FRIA
http://hdl.handle.net/2268/130961

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