The DOI matches its Crossref record; 47 of the 50 references it cites were found in a registry or an index.
Multimodal Gesture Recognition Based on the ResC3D Network
Abstract
Gesture recognition is an important issue in computer vision. Recognizing gestures with videos remains a challenging task due to the barriers of gesture-irrelevant factors. In this paper, we propose a multimodal gesture recognition method based on a ResC3D network. One key idea is to find a compact and effective representation of video sequences. Therefore, the video enhancement techniques, such as Retinex and median filter are applied to eliminate the illumination variation and noise in the input video, and a weighted frame unification strategy is utilized to sample key frames. Upon these representations, a ResC3D network, which leverages the advantages of both residual and C3D model, is developed to extract features, together with a canonical correlation analysis based fusion scheme for blending features. The performance of our method is evaluated in the Chalearn LAP isolated gesture recognition challenge. It reaches 67.71% accuracy and ranks the 1st place in this challenge.
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“In [98], the authors developed a multimodal gesture recognition system using the 3D Residual CNN (ResC3D) model [99] trained on an RGB-D dataset.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [98]
Citations
Cites
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Research works printed but not linked: 2
- konecný, 2014, One-shot-learning gesture recognition using hog-hof, Journal of Machine Learning Research, 15, 2513
- zhu, 2017, Multimodal gesture recognition using 3d convolution and convolutional lst-m, IEEE Access
Cited by
Sorted by citations
Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456
MMTM: Multimodal Transfer Module for CNN Fusion
Joze, H. R. V., Shaban, A., Iuzzolino, M. L., & Koishida, K. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01330 · Cited by 337
Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition With Multimodal Training
Abavisani, M., Joze, H. R. V., & Patel, V. M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00126 · Cited by 190
Gesture Recognition: Focus on the Hands
Narayana, P., Beveridge, J. R., & Draper, B. A. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00549 · Cited by 155
Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture Recognition
Köpüklü, O., Köse, N., & Rigoll, G. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00284 · Cited by 141
Metadata
- Authors
- Miao, Q., Li, Y., Ouyang, W., Ma, Z., Xu, X., Shi, W., & Cao, X.
- Affiliations
- Xidian University (CN)
- Chinese University of Hong Kong (HK)
- Chinese Academy of Sciences (CN)
- Venue
- 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 3047-3055
- ISSN
- 2473-9936 (ISSN-L)
- OpenAlex
- W2770472008
- Cited by
- 168
- References
- 50
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 22 · 2023: 17 · 2022: 23 · 2021: 27 · 2020: 23 · 2019: 28 · 2018: 10
- FWCI
- 12.61
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Computer Vision Workshops (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- gesture recognition, multimodal fusion, Retinex, median filter, canonical correlation analysis, feature fusion, illumination variation, noise reduction
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