Space-time Pose Representation for 3D Human ...
Document type :
Communication dans un congrès avec actes
Title :
Space-time Pose Representation for 3D Human Action Recognition
Author(s) :
Devanne, Maxime [Auteur correspondant]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Dipartimento di Sistemi e Informatica [DSI]
Wannous, Hazem [Auteur]
FOX MIIRE [LIFL]
Berretti, Stefano [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Pala, Pietro [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Daoudi, Mohamed [Auteur]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
del Bimbo, Alberto [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Dipartimento di Sistemi e Informatica [DSI]
Wannous, Hazem [Auteur]

FOX MIIRE [LIFL]
Berretti, Stefano [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Pala, Pietro [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Daoudi, Mohamed [Auteur]

FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
del Bimbo, Alberto [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Conference title :
ICIAP Workshop on Social Behaviour Analysis
City :
Naples
Country :
France
Start date of the conference :
2013-09-10
Book title :
ICIAP Workshop on Social Behaviour Analysis
Publication date :
2013-09-10
HAL domain(s) :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
English abstract : [en]
3D human action recognition is an important current challenge at the heart of many research areas lying to the modeling of the spatio-temporal information. In this paper, we propose representing human actions using ...
Show more >3D human action recognition is an important current challenge at the heart of many research areas lying to the modeling of the spatio-temporal information. In this paper, we propose representing human actions using spatio-temporal motion trajectories. In the proposed approach, each trajectory consists of one motion channel corresponding to the evolution of the 3D position of all joint coordinates within frames of action sequence. Action recognition is achieved through a shape trajectory representation that is learnt by a K-NN classifier, which takes benefit from Riemannian geometry in an open curve shape space. Experiments on the MSR Action 3D and UTKinect human action datasets show that, in comparison to state-of-the-art methods, the proposed approach obtains promising results that show the potential of our approach.Show less >
Show more >3D human action recognition is an important current challenge at the heart of many research areas lying to the modeling of the spatio-temporal information. In this paper, we propose representing human actions using spatio-temporal motion trajectories. In the proposed approach, each trajectory consists of one motion channel corresponding to the evolution of the 3D position of all joint coordinates within frames of action sequence. Action recognition is achieved through a shape trajectory representation that is learnt by a K-NN classifier, which takes benefit from Riemannian geometry in an open curve shape space. Experiments on the MSR Action 3D and UTKinect human action datasets show that, in comparison to state-of-the-art methods, the proposed approach obtains promising results that show the potential of our approach.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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