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Identifier |
000416959 |
Title |
Generative 3D hand tracking with spatially constrained pose sampling |
Alternative Title |
Τρισδιάστατη παρακολούθηση ανθρώπινων χεριών που υπόκεινται σε χωρικούς περιορισμούς |
Author
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Ροδιτάκης, Κωνσταντίνος Α.
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Thesis advisor
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Αργυρός, Αντώνιος
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Reviewer
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Παπαγιαννάκης, Γεώργιος
Ζαμπούλης, Ξενοφών
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Abstract |
Estimating the 3D pose and full articulation of a human hand based on visual information
remains a challenging task which has been intensively addressed by the research
community. The main challenges arise from the dimensionality of the problem, rapid
hand motion and self-occlusions that occur in the majority of observed poses. Reliable,
robust and accurate solutions can facilitate the development of industrial to consumerlevel
applications. There exist practical hand motion scenarios where the hand parts are
spatially constrained. In these scenarios, hand part locations can be inferred implicitly
from data-driven detectors or interaction with the environment.
In this thesis, we investigate such scenarios, and we consider this type of spatial
constraints. We present a method for 3D hand tracking that efficiently exploits spatial
constraints in the form of end effector (fingertip) locations. An end-effector target can be
either a specific 3D point or a 3D region, and the number of constrained fingertips may
vary through frames. The proposed method follows a generative, hypothesize-and-test
approach and uses a hierarchical particle filter to track the hand.
The current state of the art methods consider these spatial constraints in a soft manner
and can not guarantee that the resulting estimate will satisfy them. We tackle this issue
by enforcing spatial constraints during the hand pose hypothesis generation phase. In that
direction, we developed a simple and fast finger articulation sampling method that is
based on the concept of Reachable Distance Space (RDS).
The main contributions of this work are the following: (a) We extend the original RDS
formulation to generate finger articulations that respect both the hands' joint limits and
the end effector constraints, (b) we introduce the C-HMF framework by tightly integrating
our constraints-aware sampling strategy to the Hierarchical Model Fusion (HMF)
framework. If spatial constraints are absent at certain frames, our proposed C-HMF
framework can seamlessly fall back to the original HMF method. Each hypothesis is
evaluated by measuring the discrepancy between the rendered 3D model and the
available observations.
Several error metrics are employed to extensively evaluate our methodology on
challenging, ground truth-annotated sequences that contain severe hand occlusions.
Quantitative and qualitative results demonstrate that the proposed approach
significantly outperforms state of the art in hand tracking accuracy and robustness.
Additionally, we demonstrate that our methodology is robust to fingertip detection noise.
By exploring more densely the space of feasible solutions, we require the evaluation of
much fewer hand hypotheses, all of which satisfy the given constraints. Along with the
proposed light-weight sampling strategy, our methodology is suitable to cope with the
performance requirements of applications requiring a real time solution to the problem
of 3D hand tracking.
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Language |
English |
Subject |
3Δ παρακολούθηση χεριού |
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Co4Robots |
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Computer vision |
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RDS |
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Wearhap |
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Υπολογιστική όραση |
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Χωρικοί περιορισμοί |
Issue date |
2018-03-23 |
Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/3/1/2/metadata-dlib-1530862544-395419-3820.tkl
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Views |
369 |