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Identifier 000419336
Title A hybrid method for 3D pose estimation of personalized human body models
Alternative Title Μια υβριδική μέθοδος 3Δ οπτικής παρακολούθησης εξατομικευμένων μοντέλων του ανθρώπινου σώματος
Author Qammaz, Ammar
Thesis advisor Αργυρός, Αντώνης
Thesis advisor Παπαγιαννάκης, Γιώργος
Ζαμπούλης, Ξενοφών
Abstract We propose a new hybrid method for 3D human body pose estimation based on RGBD data. We treat this as an optimization problem that is solved using a stochastic optimization technique. The solution to the optimization problem is the pose parameters of a human model that register it to the available observations. Our method can make use of any skinned, articulated human body model. However, we focus on personalized models that can be acquired easily and automatically based on existing human scanning and mesh rigging techniques. Observations consist of the 3D structure of the human (measured by the RGBD camera) and the body joints locations (computed based on a discriminative, CNN-based component). A series of quantitative and qualitative experiments demonstrate the accuracy and the benefits of the proposed approach. In particular, we show that the proposed approach achieves state of the art results compared to competitive methods and that the use of personalized body models improve significantly the accuracy in 3D human pose estimation.
Language English
Subject Camera
Cnn
Neural
Pso
RGBD
Tracking
Νευρονικά δίκτυα
Συνελικτικά
Τρισδιάστατο
Issue date 2018-11-23
Collection   School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
Permanent Link https://elocus.lib.uoc.gr//dlib/b/d/4/metadata-dlib-1542639246-545730-21666.tkl Bookmark and Share
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