Title :

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

Author(s) :

Devanne, Maxime [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Dipartimento di Sistemi e Informatica [DSI]
Berretti, Stefano [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Pala, Pietro [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Wannous, Hazem [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Daoudi, Mohamed [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Bimbo, Alberto [Auteur]
Dipartimento di Sistemi e Informatica [DSI]

Journal title :

Pattern Recognition

Pages :

222 - 233

Publisher :

Elsevier

Publication date :

2017

ISSN :

0031-3203

English keyword(s) :

3D human behavior understanding
temporal modeling
shape space analysis
online activity detection * Corresponding author

HAL domain(s) :

Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Plateforme Recherche et Innovation dans les Environnements Visuels Numériques et Interactifs

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL