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One-shot gesture recognition with attention-based DTW for human-robot collaboration

Yiqun Kuang (University of Electronic Science and Technology of China, Chengdu, China)
Hong Cheng (School of Automation, Center for Robotics, Chengdu, China)
Yali Zheng (University of Electronic Science and Technology of China, Chengdu, China)
Fang Cui (University of Electronic Science and Technology of China, Chengdu, China)
Rui Huang (School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China)

Assembly Automation

ISSN: 0144-5154

Article publication date: 23 August 2019

Issue publication date: 18 February 2020

Abstract

Purpose

This paper aims to present a one-shot gesture recognition approach which can be a high-efficient communication channel in human–robot collaboration systems.

Design/methodology/approach

This paper applies dynamic time warping (DTW) to align two gesture sequences in temporal domain with a novel frame-wise distance measure which matches local features in spatial domain. Furthermore, a novel and robust bidirectional attention region extraction method is proposed to retain information in both movement and hold phase of a gesture.

Findings

The proposed approach is capable of providing efficient one-shot gesture recognition without elaborately designed features. The experiments on a social robot (JiaJia) demonstrate that the proposed approach can be used in a human–robot collaboration system flexibly.

Originality/value

According to previous literature, there are no similar solutions that can achieve an efficient gesture recognition with simple local feature descriptor and combine the advantages of local features with DTW.

Keywords

Citation

Kuang, Y., Cheng, H., Zheng, Y., Cui, F. and Huang, R. (2020), "One-shot gesture recognition with attention-based DTW for human-robot collaboration", Assembly Automation, Vol. 40 No. 1, pp. 40-47. https://doi.org/10.1108/AA-11-2018-0228

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited