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Research on Dynamic and Static Fusion Polymorphic Gesture Recognition Algorithm for Interactive Teaching Interface
Feng, Zhiquan1,2; Xu, Tao1,2; Yang, Xiaohui1,2; Tian, Jinglan1,2; Yi, Jiangyan3; Zhao, Ke4
First AuthorFeng, Zhiquan
2019
Conference Name4th International Conference on Cognitive Systems and Information Processing, ICCSIP 2018
Source PublicationCognitive Systems and Signal Processing - 4th International Conference, ICCSIP 2018, Revised Selected Papers
Volume1006
Pages104-115
Conference DateNovember 29, 2018 - December 1, 2018
Conference PlaceBeijing, China
PublisherSpringer Verlag
Contribution Rank4
AbstractIn order to solve the problem of teachers' excessive energy dissipation due to interaction with teaching equipment in traditional classrooms, an interactive and intelligent teaching interface is proposed to enable teachers to use the gestures to give students a geometry lesson. The traditional algorithm of gesture recognition mainly consists of feature extraction and classifier, which requires human-designed features. The recognition is mainly based on static gesture or dynamic gesture singular state recognition algorithm. The recognition accuracy is not robust enough and different people Identification results do not have the universality and ease of operation. In order to solve this problem, we propose a multi-state gesture recognition algorithm based on the deep learning network, which combines the large database of hand gestures and the deep learning algorithms. The innovation of this algorithm is as follows: Aiming at the static gesture images, a sequence reduction algorithm is proposed. According to the sequence of dynamic gestures, the first and last frame fixed and intermediate frame traversal combination algorithm are proposed to get the dynamic and static fusion gesture training datasets, and then the dynamic and static fusion datasets are input to the deep learning network GoogLeNet for training. After repeated training, we found the optimal rule of deep learning network training. According to the optimization law, we got GoogLeNet_model which can recognize 23 kinds of dynamic and static fusion gestures, the recognition rate is 97.09%. We use this model in interactive teaching interface, and achieved good application effect. 2019, Springer Nature Singapore Pte Ltd.
KeywordApplication effect - Gesture recognition algorithm - People identification - Recognition accuracy - Reduction algorithms - Teaching equipments - Teaching interfaces - Training data sets
Subject AreaGesture Recognition
DOI10.1007/978-981-13-7986-4_10
ISBN9789811379857
Indexed ByEI
EI Accession Number20192106943762
EI KeywordsClassification (of information) - Cognitive systems - Deep learning - Dynamics - Educational technology - Energy dissipation - Learning algorithms - Signal processing - Students
EI Classification Number525.4 Energy Losses (industrial and residential) - 716.1 Information Theory and Signal Processing - 901.2 Education
Citation statistics
Document Type会议论文
Identifierhttp://ir.psych.ac.cn/handle/311026/30036
Collection健康与遗传心理学研究室
Affiliation1.School of Information Science and Engineering, University of Jinan, Jinan; 250022, China;
2.Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan; 250022, China;
3.Institute of Computing Technology Chinese Academy of Sciences, Beijing; 100190, China;
4.Institute of Psychology Chinese Academy of Sciences, Beijing; 100101, China
Recommended Citation
GB/T 7714
Feng, Zhiquan,Xu, Tao,Yang, Xiaohui,et al. Research on Dynamic and Static Fusion Polymorphic Gesture Recognition Algorithm for Interactive Teaching Interface[C]:Springer Verlag,2019:104-115.
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