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Identifier 000402827
Title Full - body pose tracking under severe occlusions - the top view reprojection approach
Alternative Title Παρακολούθηση της πόζας ανθρώπινου σώματος υπό συνθήκες έντονων επικαλύψεων - προσέγγιση με χρήση προβολής κάτοψης
Author Σιγάλας, Μάρκος Μαρίνος
Thesis advisor Τραχανιάς, Παναγιώτης
Reviewer Αργυρός, Αντώνιος
Λουράκης, Εμμανουήλ
Ζαμπούλης, Ξενοφών
Παπαγιαννάκης, Γεώργιος
Vincze, Marcus
Santos, Jose
Abstract Marker-less articulated human body pose recovery and tracking is a challenging problem of great importance, with strong theoretical and practical implications. The recent introduction of low-cost depth cameras triggered a number of interesting new works, pushing forward the state of the art. However, despite the remarkable progress, estimating the body pose in realistic, complex scenarios is still an open research task. In this thesis we propose and develop a markerless model-based method to recover and track the full body pose, from RGB-D sequences, in arbitrary scenarios where users can freely enter or leave the scene, move, act and interact with other users or the environment. Our research focuses mainly on the problem of handling occlusions, either across body parts belonging to the same user, or across different users. At the same time, we attempt to tackle additional important issues encountered in the problem at hand, such as dealing with the large diversity of human bodies or the unconstrained initialization of tracking. Towards this goal, we introduced the novel concept of Top View Reprojection (TVR) of cylindrical objects, which uniquely defines the pose of a cylinder based on certain quantitative appearance properties of its Top View, i.e. the view aligned with the cylinder's main axis. Based on this, the problem of estimating the pose of a cylindrical object becomes that of estimating the corresponding Top View. Interestingly, the developed formulation of TVR remains unaffected from factors such as noisy or missing data. Capitalizing on the TVR concept, we represent the human body by a cylinder-based model, consisting of 11 body parts. The body is uniformly treated within the TVR framework following a local optimization technique; body parts, represented as cylinders, are examined in a top-to-bottom sequential order, starting from the head. For each body part a set of hypotheses is generated and tracked over time by a Particle Filter (PF). To evaluate each hypothesis, we employ a novel metric that considers the virtual Top View of the corresponding body part. The latter, in conjunction with regular depth information, effectively copes with difficult and ambiguous cases, such as severe inter-and intra-person occlusions. For evaluation purposes, we conducted several series of experiments addressing realistic scenarios of gradually increased difficulty, involving varying number of users interacting with each other. We further compared the performance of the proposed method against that of state-of-the-art approaches using public or own-collected datasets with ground truth annotation. The presented quantitative and qualitative results attest for the effectiveness of our approach.
Language English
Issue date 2015-07-07
Collection   Faculty/Department--Faculty of Sciences and Engineering--Department of Computer Science--Doctoral theses
  Type of Work--Doctoral theses
Permanent Link https://elocus.lib.uoc.gr//dlib/2/0/3/metadata-dlib-1473933410-162575-3946.tkl Bookmark and Share
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