SHREC 2021: Skeleton-based hand gesture recognition in the wild
| Autor: | Minh-Quan Le, Joseph J. LaViola, Andrea Giachetti, Andrea D'Eusanio, Hai-Dang Nguyen, Stefano Pini, Minh-Triet Tran, Simone Soso, Deborah Pintani, Marina Monti, Katia Lupinetti, Rita Cucchiara, Franca Giannini, Andrea Ranieri, Alessandro Simoni, Ariel Caputo, Roberto Vezzani, Mehran Maghoumi, Guido Borghi |
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| Přispěvatelé: | Caputo Ariel, Giacchetti Andrea, Soso Simone, Pintani Deborah, D'Eusanio Andrea, Pini Stefano, Borghi G, Simoni Alessandro, Vezzani Roberto, Cucchiara Rita, Ranieri Andrea, Giannini Franca, Lupinetti Katia, Monti Marina, Maghoumi Mehran, LaViola Jr Joseph, Le Minh-Quan, Nguyen Hai-Dang, Tran Minh-Triet |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Online Interaction
Interaction business.industry Computer science BitTorrent tracker General Engineering Oculus Video processing hand gestures 3D keypoints Computer Graphics and Computer-Aided Design Mixed reality Human-Computer Interaction Gesture recognition Software Hand skeleton Human–computer interaction Online business Set (psychology) Gesture Gesture recognition Hand skeleton Online Interaction |
| Zdroj: | Computers & graphics 99 (2021): 201–211. doi:10.1016/j.cag.2021.07.007 info:cnr-pdr/source/autori:A. Caputo, A. Giachetti, S. Soso, D. Pintani, A. D'Eusanio, S. Pini, G. Borghi, A. Simoni, R. Vezzani, R. Cucchiara, A. Ranieri, F. Giannini, K. Lupinetti, M. Monti, M. Maghoumi, J.J. LaViola Jr, M.-Q. Le, H.-D. Nguyen, M.-T. Tran/titolo:SHREC 2021: Skeleton-based hand gesture recognition in the wild/doi:10.1016%2Fj.cag.2021.07.007/rivista:Computers & graphics/anno:2021/pagina_da:201/pagina_a:211/intervallo_pagine:201–211/volume:99 |
| DOI: | 10.1016/j.cag.2021.07.007 |
| Popis: | Gesture recognition is a fundamental tool to enable novel interaction paradigms in a variety of application scenarios like Mixed Reality environments, touchless public kiosks, entertainment systems, and more. Recognition of hand gestures can be nowadays performed directly from the stream of hand skeletons estimated by software provided by low-cost trackers (Ultraleap) and MR headsets (Hololens, Oculus Quest) or by video processing software modules (e.g. Google Mediapipe). Despite the recent advancements in gesture and action recognition from skeletons, it is unclear how well the current state-of-the-art techniques can perform in a real-world scenario for the recognition of a wide set of heterogeneous gestures, as many benchmarks do not test online recognition and use limited dictionaries. This motivated the proposal of the SHREC 2021: Track on Skeleton-based Hand Gesture Recognition in the Wild. For this contest, we created a novel dataset with heterogeneous gestures featuring different types and duration. These gestures have to be found inside sequences in an online recognition scenario. This paper presents the result of the contest, showing the performances of the techniques proposed by four research groups on the challenging task compared with a simple baseline method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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