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Spatial-temporal attention res-TCN for skeleton-based dynamic hand gesture recognition

Hou, J; Wang, G; Chen, X; Xue, JH; Zhu, R; Yang, H; (2019) Spatial-temporal attention res-TCN for skeleton-based dynamic hand gesture recognition. In: Leal-Taixé, L and Roth, S, (eds.) Computer Vision – ECCV 2018 Workshops. (pp. pp. 273-286). Springer Nature: Cham, Switzerland.

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Abstract

Dynamic hand gesture recognition is a crucial yet challenging task in computer vision. The key of this task lies in an effective extraction of discriminative spatial and temporal features to model the evolutions of different gestures. In this paper, we propose an end-to-end Spatial-Temporal Attention Residual Temporal Convolutional Network (STA-Res-TCN) for skeleton-based dynamic hand gesture recognition, which learns different levels of attention and assigns them to each spatial-temporal feature extracted by the convolution filters at each time step. The proposed attention branch assists the networks to adaptively focus on the informative time frames and features while exclude the irrelevant ones that often bring in unnecessary noise. Moreover, our proposed STA-Res-TCN is a lightweight model that can be trained and tested in an extremely short time. Experiments on DHG-14/28 Dataset and SHREC’17 Track Dataset show that STA-Res-TCN outperforms state-of-the-art methods on both the 14 gestures setting and the more complicated 28 gestures setting.

Type: Proceedings paper
Title: Spatial-temporal attention res-TCN for skeleton-based dynamic hand gesture recognition
Event: ECCV: European Conference on Computer Vision
Location: Munich, Germany
Dates: 8th-14th September 2018
ISBN-13: 978-3-030-11023-9
DOI: 10.1007/978-3-030-11024-6_18
Publisher version: https://doi.org/10.1007/978-3-030-11024-6_18
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Dynamic hand gesture recognition, Spatial-Temporal Attention, Temporal Convolutional Networks
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: http://discovery.ucl.ac.uk/id/eprint/10069975
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