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A novel dynamic gesture understanding algorithm fusing convolutional neural networks with hand-crafted features

文献类型：期刊论文

作者Liu, Yanhong3,4; Song, Shouan3,4; Yang, Lei3,4; Bian, Guibin2,4; Yu, Hongnian1,4
刊名JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
出版日期2022-02-01
卷号83页码:11
关键词Dynamic gesture understanding Transfer learning Feature fusion Dempster-Shafer evidence theory Support vector machine
ISSN号1047-3203
DOI10.1016/j.jvcir.2022.103454
通讯作者Yang, Lei(leiyang2019@zzu.edu.cn)
英文摘要Dynamic gestures have attracted much attention in recent years due to their user-friendly interactive characteristics. However, accurate and efficient dynamic gesture understanding remains a challenge due to complex scenarios and motion information. Conventional handcrafted features are computationally cheap but can only extract low-level image features. This leads to performance degradation when dealing with complex scenes. In contrast, deep learning-based methods have a stronger feature expression ability and hence can capture more abstract and high-level image features. However, they critically rely on a large amount of training data. To address the above issues, a novel dynamic gesture understanding algorithm based on feature fusion is proposed for accurate dynamic gesture prediction. It leverages the advantages of handcrafted features and transfer learning. Aimed at small-scale dynamic gesture data, transfer learning is introduced for capturing effective feature expression. To precisely model the critical temporal information associated with dynamic gestures, a novel feature descriptor, namely, AlexNet(2), is proposed for effective feature expression of dynamic gestures from the spatial and temporal domain. On this basis, a decision-level feature fusion framework based on support vector machine (SVM) and Dempster-Shafer (DS) evidence theory is constructed to utilize handcrafted features and AlexNet(2) to realize high-precision dynamic gesture understanding. To verify the effectiveness and robustness of the proposed recognition algorithm, analysis and comparison experiments are performed on the public Cambridge gesture dataset and Northwestern University hand gesture dataset. The proposed gesture recognition algorithm achieves prediction accuracies of 99.50% and 96.97% on these two datasets. Experimental results show that the proposed recognition framework exhibits a better recognition performance in comparison with related prediction algorithms.
WOS关键词RECOGNITION ; EXTRACTION ; MATRIX ; SHAPE
资助项目National Natural Science Foundation of China[62003309] ; National Key Research & Development Project of China[2020YFB1313701] ; Science & Technology Research Project in Henan Province of China[202102210098] ; Outstanding Foreign Scientist Support Project in Henan Province of China[GZS2019008]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000819856800004
出版者ACADEMIC PRESS INC ELSEVIER SCIENCE
资助机构National Natural Science Foundation of China ; National Key Research & Development Project of China ; Science & Technology Research Project in Henan Province of China ; Outstanding Foreign Scientist Support Project in Henan Province of China
源URL[http://ir.ia.ac.cn/handle/173211/49210]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者Yang, Lei
作者单位1.Edinburgh Napier Univ, Built Environm, Edinburgh EH10 5DT, Midlothian, Scotland
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Robot Percept & Control Engn Lab Henan Prov, Zhengzhou 450001, Peoples R China
4.Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Henan, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Yanhong,Song, Shouan,Yang, Lei,et al. A novel dynamic gesture understanding algorithm fusing convolutional neural networks with hand-crafted features[J]. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION,2022,83:11.
APA Liu, Yanhong,Song, Shouan,Yang, Lei,Bian, Guibin,&Yu, Hongnian.(2022).A novel dynamic gesture understanding algorithm fusing convolutional neural networks with hand-crafted features.JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION,83,11.
MLA Liu, Yanhong,et al."A novel dynamic gesture understanding algorithm fusing convolutional neural networks with hand-crafted features".JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 83(2022):11.

入库方式： OAI收割

来源：自动化研究所

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