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Identifier

000423801

Title

3D hand tracking by employing probabilistic principal component analysis to model action priors

Alternative Title

3Δ παρακολούθηση του ανθρώπινου χεριού με χρήση πιθανοτικής ανάλυσης κύριων συνιστωσών για τη μοντελοποίηση της δραστηριότητάς του

Author

Πορφυράκης, Εμμανουήλ- Ούλωφ Τ.

Thesis advisor

Αργυρός, Αντώνιος

Reviewer

Παπαγιαννάκης, Γεώργιος
Παναγιωτάκης, Κώστας

Abstract

One important problem in computer vision is the estimation of the 3D pose and full articulation of a human hand based on visual information. The solution of this problem can facilitate the development of many applications such as human-computer interaction, robot teleoperation and others. The main challenges concern the high dimensionality of the problem, the occlusions due to the hand geometry or due to the manipulation of objects, the fast hand motion and the variability of the illumination conditions and the scene context. This thesis addresses the problem of 3D hand pose estimation by modeling specific hand actions using a dimensionality reduction technique, the probabilistic Principal Component Analysis. For each of the considered actions, a parametric subspace is learnt based on a dataset of sample action executions. We developed two hand trackers that can perform 3D hand pose estimation either in the case of unconstraint hand motion or in the case that the hand is engaged in some of the modelled actions. The first tracker is based on particle filtering while the second is based on gradient descent optimization. In both cases the goal is to fit a 3D hand model to the available observations. Both methods employ an online criterion for automatically switching between tracking the hand in the unconstrained case and tracking it in the case of learnt action sub-spaces. To train and evaluate the proposed methods, we developed a new dataset that contains sample executions of 5 different grasp-like hand actions and hand/object interactions. We tested the proposed methods both quantitatively and qualitatively. For the quantitative evaluation we relied on our dataset to create synthetic sequences from which we artificially removed observations to simulate occlusions. The obtained results show that the proposed methods improve 3D hand pose estimation over existing approaches especially in the presence of occlusions, where the employed action models assist the accurate recovery of the 3D hand pose despite the missing observations.

Language

English

Subject

CPPCA

Data training

Εκπαίδευση δεδομένων

Issue date

2019-07-26

Collection

School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses

Type of Work--Post-graduate theses

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