Research on Dynamic and Static Fusion Polymorphic Gesture Recognition Algorithm for Interactive Teaching Interface
文献类型：会议论文
| 作者 | Feng, Zhiquan1,2; Xu, Tao1,2; Yang, Xiaohui1,2; Tian, Jinglan1,2; Yi, Jiangyan3; Zhao, Ke4
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| 出版日期 | 2019 |
| 会议日期 | November 29, 2018 - December 1, 2018 |
| 会议地点 | Beijing, China |
| 关键词 | Application effect - Gesture recognition algorithm - People identification - Recognition accuracy - Reduction algorithms - Teaching equipments - Teaching interfaces - Training data sets |
| 卷号 | 1006 |
| DOI | 10.1007/978-981-13-7986-4_10 |
| 页码 | 104-115 |
| 英文摘要 | In 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. |
| 产权排序 | 4 |
| 会议录 | Cognitive Systems and Signal Processing - 4th International Conference, ICCSIP 2018, Revised Selected Papers
 |
| 会议录出版者 | Springer Verlag |
| 学科主题 | Gesture Recognition |
| ISBN号 | 9789811379857 |
| 源URL | [http://ir.psych.ac.cn/handle/311026/30036]  |
| 专题 | 心理研究所_健康与遗传心理学研究室 |
| 作者单位 | 1.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 |
| 推荐引用方式 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]. 见:. Beijing, China. November 29, 2018 - December 1, 2018. |
入库方式： OAI收割
来源：心理研究所
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