Analyse et reconnaissance de séquences vidéos d'activités humaines dans l'espace sémantique

Abstract : This thesis focuses on the characterization and recognition of human activities in videos. This research domain is motivated by a large set of applications such as automatic video indexing, video monitoring or elderly assistance. In the first part of our work, we develop an approach based on the optical flow estimation in video to recognize human elementary actions. From the obtained vector field, we extract critical points and trajectories estimated at different spatio-temporal scales. The late fusion of local characteristics such as motion orientation and shape around critical points, combined with the frequency description of trajectories allow us to obtain one of the best recognition rate among state of art methods. In a second part, we develop a method for recognizing complex human activities by considering them as temporal sequences of elementary actions. In a first step, elementary action probabilities over time is calculated in a video sequence with our first approach. Vectors of action probabilities lie in a statistical manifold called semantic simplex. Activities are then represented as trajectories on this manifold. Finally, a new descriptor is introduced to discriminate between activities from the shape of their associated trajectories. This descriptor takes into account the induced geometry of the simplex manifold.
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Submitted on : Tuesday, December 12, 2017 - 12:04:04 AM
Last modification on : Thursday, May 17, 2018 - 4:12:10 AM


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Cyrille Beaudry. Analyse et reconnaissance de séquences vidéos d'activités humaines dans l'espace sémantique. Vision par ordinateur et reconnaissance de formes [cs.CV]. Université de La Rochelle, 2015. Français. ⟨NNT : 2015LAROS042⟩. ⟨tel-01661437⟩



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