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Computer Science > Computer Vision and Pattern Recognition

arXiv:2207.06706 (cs)
[Submitted on 14 Jul 2022 (v1), last revised 22 Jul 2022 (this version, v2)]

Title:SHREC 2022 Track on Online Detection of Heterogeneous Gestures

Authors:Ariel Caputo, Marco Emporio, Andrea Giachetti, Marco Cristani, Guido Borghi, Andrea D'Eusanio, Minh-Quan Le, Hai-Dang Nguyen, Minh-Triet Tran, F. Ambellan, M. Hanik, E. Nava-Yazdani, C. von Tycowicz
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Abstract:This paper presents the outcomes of a contest organized to evaluate methods for the online recognition of heterogeneous gestures from sequences of 3D hand poses. The task is the detection of gestures belonging to a dictionary of 16 classes characterized by different pose and motion features. The dataset features continuous sequences of hand tracking data where the gestures are interleaved with non-significant motions. The data have been captured using the Hololens 2 finger tracking system in a realistic use-case of mixed reality interaction. The evaluation is based not only on the detection performances but also on the latency and the false positives, making it possible to understand the feasibility of practical interaction tools based on the algorithms proposed. The outcomes of the contest's evaluation demonstrate the necessity of further research to reduce recognition errors, while the computational cost of the algorithms proposed is sufficiently low.
Comments: Accepted on Computer & Graphics journal
Subjects: Computer Vision and Pattern Recognition (cs.CV)
MSC classes: 68T10
ACM classes: I.5.2
Cite as: arXiv:2207.06706 [cs.CV]
  (or arXiv:2207.06706v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2207.06706

Submission history

From: Ariel Caputo [view email]
[v1] Thu, 14 Jul 2022 07:24:02 UTC (3,857 KB)
[v2] Fri, 22 Jul 2022 11:51:49 UTC (3,853 KB)
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