Cited by 168Hand Gesture Recognition SystemsHuman Pose and Action RecognitionGait Recognition and Analysis

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Multimodal Gesture Recognition Based on the ResC3D Network

Miao, Qiguang, Li, Yunan, Ouyang, Wanli, Ma, Zhenxin, Xu, Xin, Shi, Weikang, Cao, Xiaochun·2017·2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

DOI 10.1109/iccvw.2017.360

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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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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)
ORCID: Miao, Qiguang
Li, Yunan
Ouyang, Wanli
Xu, Xin
Shi, Weikang
Cao, Xiaochun
Venue: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
Publisher: IEEE
Published: 2017-10
Pages: 3047-3055
ISSN: 2473-9936 (ISSN-L)
DOI: 10.1109/iccvw.2017.360
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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Multimodal Gesture Recognition Based on the ResC3D Network

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