论文标题

SHREC 2022在线检测异质手势的曲目

SHREC 2022 Track on Online Detection of Heterogeneous Gestures

论文作者

Caputo, Ariel, Emporio, Marco, Giachetti, Andrea, Cristani, Marco, Borghi, Guido, D'Eusanio, Andrea, Le, Minh-Quan, Nguyen, Hai-Dang, Tran, Minh-Triet, Ambellan, F., Hanik, M., Nava-Yazdani, E., von Tycowicz, C.

论文摘要

本文介绍了组织的竞赛的结果,以评估3D手姿势序列中异质手势的在线识别方法的方法。任务是检测属于以不同姿势和运动特征为特征的16个类词典的手势。该数据集具有手跟踪数据的连续序列,其中手势与非显着的动作交织在一起。在现实的混合现实交互用例中,使用HoloLens 2手指跟踪系统捕获了数据。评估不仅基于检测性能,还基于延迟和误报,使您可以根据提出的算法了解实用相互作用工具的可行性。比赛评估的结果表明需要进一步研究以减少识别错误,而提出的算法的计算成本足够低。

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.

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