This paper presents the results of the Eurographics 2019 SHape Retrieval Contest track on online gesture recognition. The goal of this contest was to test state-of-the-art methods that can be used to online detect command gestures from hands' movements tracking on a basic benchmark where simple gestures are performed interleaving them with other actions. Unlike previous contests and benchmarks on trajectory-based gesture recognition, we proposed an online gesture recognition task, not providing pre-segmented gestures, but asking the participants to find gestures within recorded trajectories. The results submitted by the participants show that an online detection and recognition of sets of very simple gestures from 3D trajectories captured with a cheap sensor can be effectively performed. The best methods proposed could be, therefore, directly exploited to design effective gesture-based interfaces to be used in different contexts, from Virtual and Mixed reality applications to the remote control of home devices.
SHREC 2019 Track: Online Gesture Recognition / Caputo, F. M.; Burato, S.; Pavan, G.; Voillemin, T.; Wannous, H.; Vandeborre, J. P.; Maghoumi, M.; Taranta, E. M.; Razmjoo, A.; J. J. LaViola Jr., ; Manganaro, Fabio; Pini, S.; Borghi, G.; Vezzani, R.; Cucchiara, R.; Nguyen, H.; Tran, M. T.; Giachetti, A.. - 160897:(2019), pp. 93-102. (Intervento presentato al convegno 12th Eurographics Workshop on 3D Object Retrieval, 3DOR 2019, in conjunction with the 40th Annual Conference of the European Association for Computer Graphics, EG 2019 tenutosi a Genova nel 5-6 May 2019) [10.2312/3dor.20191067].
SHREC 2019 Track: Online Gesture Recognition
MANGANARO, FABIO;S. Pini;G. Borghi;R. Vezzani;R. Cucchiara;
2019
Abstract
This paper presents the results of the Eurographics 2019 SHape Retrieval Contest track on online gesture recognition. The goal of this contest was to test state-of-the-art methods that can be used to online detect command gestures from hands' movements tracking on a basic benchmark where simple gestures are performed interleaving them with other actions. Unlike previous contests and benchmarks on trajectory-based gesture recognition, we proposed an online gesture recognition task, not providing pre-segmented gestures, but asking the participants to find gestures within recorded trajectories. The results submitted by the participants show that an online detection and recognition of sets of very simple gestures from 3D trajectories captured with a cheap sensor can be effectively performed. The best methods proposed could be, therefore, directly exploited to design effective gesture-based interfaces to be used in different contexts, from Virtual and Mixed reality applications to the remote control of home devices.File | Dimensione | Formato | |
---|---|---|---|
093-1022.pdf
Open access
Tipologia:
Versione originale dell'autore proposta per la pubblicazione
Dimensione
1.3 MB
Formato
Adobe PDF
|
1.3 MB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris