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.

Motion Segment Decomposition of RGB-D Sequences for Human Behavior Understanding / Devanne, Maxime; Berretti, Stefano; Pala, Pietro; Wannous, Hazem; Daoudi, Mohamed; Del Bimbo, Alberto. - In: PATTERN RECOGNITION. - ISSN 0031-3203. - STAMPA. - 61:(2017), pp. 222-233. [10.1016/j.patcog.2016.07.041]

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

BERRETTI, STEFANO;PALA, PIETRO;DEL BIMBO, ALBERTO
2017

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.
2017
61
222
233
Devanne, Maxime; Berretti, Stefano; Pala, Pietro; Wannous, Hazem; Daoudi, Mohamed; Del Bimbo, Alberto
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1053997
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