Motion Segments Decomposition of RGB-D Sequences for Human Behavior Understanding

Maxime Devanne 1, 2 Stefano Berretti 1 Pietro Pala 1 Hazem Wannous 2 Mohamed Daoudi 2 Alberto Bimbo 1
2 3D-SAM - Modeling and Analysis of Static and Dynamic Shapes
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : In this paper, we propose a framework for analyzing and understanding human behavior from depth videos. The proposed solution first employs shape analysis of the human pose across time to decompose the full motion into short temporal segments representing elementary motions. Then, each segment is characterized by human motion and depth appearance around hand joints to describe the change in pose of the body and the interaction with objects. Finally , the sequence of temporal segments is modeled through a Dynamic Naive Bayes classifier, which captures the dynamics of elementary motions characterizing human behavior. Experiments on four challenging datasets evaluate the potential of the proposed approach in different contexts, including gesture or activity recognition and online activity detection. Competitive results in comparison with state of the art methods are reported.
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Pattern Recognition, Elsevier, 2017, 61, pp.222 - 233. 〈10.1016/j.patcog.2016.07.041〉
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Maxime Devanne, Stefano Berretti, Pietro Pala, Hazem Wannous, Mohamed Daoudi, et al.. Motion Segments Decomposition of RGB-D Sequences for Human Behavior Understanding. Pattern Recognition, Elsevier, 2017, 61, pp.222 - 233. 〈10.1016/j.patcog.2016.07.041〉. 〈hal-01521148〉

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