Multimodal Gesture Recognition Using Multi-stream Recurrent Neural Network
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A multi-stream architecture built on an RNN (LSTM) was proposed to capture spatial-temporal features from gesture data.
“Regarding the multimodal dimensions of gesture recognition, the authors of [97] proposed a multi-stream architecture based on the RNN (LSTM) model to capture spatial-temporal features from gesture data.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [97]
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- Authors
- Nishida, N., & Nakayama, H.
- Affiliations
- The University of Tokyo (JP)
- Corresponding author
- Nishida, Noriki
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 682-694
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2405546895
- Cited by
- 53
- References
- 35
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 2 · 2022: 4 · 2021: 9 · 2020: 3 · 2019: 10 · 2018: 11 · 2017: 7 · 2016: 4
- FWCI
- 7.28
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (26730085)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- multimodal gesture recognition, LSTM, end-to-end learning, Kinect sensor, noise robustness
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