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Reconnaissance en-ligne d’actions 3D par l’analyse des trajectoires du squelette humain

Abstract : The objective of this thesis is to design an original transparent approach able to detect in real time the occurrence of an action (3D gesture), in an unsegmented flow and ideally as early as possible. This work is part of a collaboration between two IRISA-Inria teams in Rennes, namely Intuidoc and MimeTIC. By taking advantage of the complementary expertise of the two research teams, we propose to reconsider the needs and difficulties encountered to model, recognize and detect a 3D action by proposing new solutions in the light of the advances made in terms of 2D handwriting modeling. The contributions of this thesis are grouped into three main parts. In the first part, we propose a new approach to model and recognize a pre-segmented action. Indeed, it is first necessary to develop a representation able to characterize as finely as possible a given action to facilitate recognition. In the second part, we introduce an approach to recognize an action in an unsegmented flow: without prior knowledge of the beginning or the end of the action. Finally, in the third part, we extend this last approach for the early characterization of an action with very little information (ie the beginning of the action). For each of these three issues, we have explicitly identified the difficulties to be considered in order to make a complete description of them so that we can design targeted solutions for each of them. The experimental results obtained on different benchmarks of actions attest to the validity of our approach. In addition, through collaborations that took place during the thesis, the developed approaches were deployed in three applications, including applications in animation and in dynamic hand gestures recognition.
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https://hal.inria.fr/tel-01857262
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Submitted on : Tuesday, August 14, 2018 - 5:01:15 PM
Last modification on : Friday, March 6, 2020 - 4:32:02 PM
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  • HAL Id : tel-01857262, version 1

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Said Yacine Boulahia. Reconnaissance en-ligne d’actions 3D par l’analyse des trajectoires du squelette humain. Vision par ordinateur et reconnaissance de formes [cs.CV]. INSA Rennes, 2018. Français. ⟨tel-01857262v1⟩

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