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ChaLearn Looking at People: IsoGD and ConGD Large-Scale RGB-D Gesture Recognition

文献类型：期刊论文

作者Wan, Jun15,16; Lin, Chi14; Wen, Longyin13; Li, Yunan11,12; Miao, Qiguang11,12; Escalera, Sergio10; Anbarjafari, Gholamreza7,8,9; Guyon, Isabelle5,6; Guo, Guodong3,4; Li, Stan Z.1,2
刊名IEEE TRANSACTIONS ON CYBERNETICS
出版日期2022-05-01
卷号52期号:5页码:3422-3433
关键词Gesture recognition Measurement Task analysis Training Conferences Computer vision Bidirectional long short-term memory (Bi-LSTM) gesture recognition RGB-D
ISSN号2168-2267
DOI10.1109/TCYB.2020.3012092
通讯作者Wan, Jun(jun.wan@ia.ac.cn)
英文摘要The ChaLearn large-scale gesture recognition challenge has run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. It describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. In this article, we discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition and provide a detailed analysis of the current methods for large-scale isolated and continuous gesture recognition. In addition to the recognition rate and mean Jaccard index (MJI) as evaluation metrics used in previous challenges, we introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) method, determining video division points based on skeleton points. Experiments show that the proposed Bi-LSTM outperforms state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR.
WOS关键词FUSION
资助项目Chinese National Natural Science Foundation[61961160704] ; Chinese National Natural Science Foundation[61876179] ; Key Project of the General Logistics Department[ASW17C001] ; Science and Technology Development Fund of Macau[0010/2019/AFJ] ; Science and Technology Development Fund of Macau[0025/2019/AKP] ; Science and Technology Development Fund of Macau[PID2019-105093GB-I00] ; (MINECO/FEDER, UE) ; (CERCA Programme/Generalitat de Catalunya) ; ICREA through the ICREA Academia Programme - European Regional Development Fund
WOS研究方向Automation & Control Systems ; Computer Science
语种英语
WOS记录号WOS:000798227800076
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Chinese National Natural Science Foundation ; Key Project of the General Logistics Department ; Science and Technology Development Fund of Macau ; (MINECO/FEDER, UE) ; (CERCA Programme/Generalitat de Catalunya) ; ICREA through the ICREA Academia Programme - European Regional Development Fund
源URL[http://ir.ia.ac.cn/handle/173211/49482]  
专题自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心
通讯作者Wan, Jun
作者单位1.Macau Univ Sci & Technol, Taipa, Macau, Peoples R China
2.Westlake Univ, Hangzhou 310024, Peoples R China
3.Natl Engn Lab Deep Learning Technol & Applicat, Beijing 100193, Peoples R China
4.Baidu Res, Inst Deep Learning, Beijing 100193, Peoples R China
5.Univ Paris Saclay, F-91190 St Aubin, France
6.ChaLearn, San Francisco, CA 94115 USA
7.Hasan Kalyoncu Univ, Fac Engn, TR-27100 Gaziantep, Turkey
8.PwC Finland, Helsinki 00180, Finland
9.Univ Tartu, Inst Technol, iCV Lab, EE-50090 Tartu, Estonia
10.Univ Barcelona, Comp Vis Ctr, Barcelona 08007, Spain
推荐引用方式
GB/T 7714		 Wan, Jun,Lin, Chi,Wen, Longyin,et al. ChaLearn Looking at People: IsoGD and ConGD Large-Scale RGB-D Gesture Recognition[J]. IEEE TRANSACTIONS ON CYBERNETICS,2022,52(5):3422-3433.
APA Wan, Jun.,Lin, Chi.,Wen, Longyin.,Li, Yunan.,Miao, Qiguang.,...&Li, Stan Z..(2022).ChaLearn Looking at People: IsoGD and ConGD Large-Scale RGB-D Gesture Recognition.IEEE TRANSACTIONS ON CYBERNETICS,52(5),3422-3433.
MLA Wan, Jun,et al."ChaLearn Looking at People: IsoGD and ConGD Large-Scale RGB-D Gesture Recognition".IEEE TRANSACTIONS ON CYBERNETICS 52.5(2022):3422-3433.

入库方式： OAI收割

来源：自动化研究所

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