Dynamic gesture recognition method based on Improved R(2+1)D

Yupeng Huo, Jie Shen, Sheng Zhang, Li Wang

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

Currently, methods based on 3D convolutional neural networks have made significant progress in the field of dynamic gesture recognition. Dynamic gestures are highly redundant in both the temporal and spatial dimensions, and the complex environment during the recognition process can easily affect the final recognition results. Therefore, it is crucial to make the model focus on the important moments and regions of gesture movements and extract relevant salient spatiotemporal features to further improve model performance. To address this issue, this paper proposes a lightweight Temporal-Spatial-Channel attention (TSCA) module based on the R(2+1)D network. The module consists of two sub-modules: a Temporal-Channel attention (TCA) module and a Temporal-Spatial attention (TSA) module, with the goal of enabling the model to focus on important information along the spatial, channel, and temporal dimensions during gesture movements. Finally, the TSCA attention module is integrated into the R(2+1)D network, resulting in only a 2.8M increase in parameters, and achieves good performance on the IPN-Hand and NvGesture datasets.

源语言英语
主期刊名2023 4th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2023
出版商Institute of Electrical and Electronics Engineers Inc.
323-328
页数6
ISBN(电子版)9798350326444
DOI
出版状态已出版 - 2023
活动4th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2023 - Zhuhai, 中国
期限: 12 5月 202314 5月 2023

出版系列

姓名2023 4th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2023

会议

会议4th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2023
国家/地区中国
Zhuhai
时期12/05/2314/05/23

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