Title
Shrec 2021: Skeleton-Based Hand Gesture Recognition In The Wild
Abstract
Gesture recognition is a fundamental tool to enable novel interaction paradigms in a variety of appli-cation 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 skele-tons 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 advance-ments 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. (c) 2021 Elsevier Ltd. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.cag.2021.07.007
COMPUTERS & GRAPHICS-UK
Keywords
DocType
Volume
Gesture recognition, Hand skeleton, Online, Interaction
Journal
99
ISSN
Citations 
PageRank 
0097-8493
1
0.35
References 
Authors
0
19