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

doi:10.1016/j.jvcir.2022.103454

	A novel dynamic gesture understanding algorithm fusing convolutional neural networks with hand-crafted features
	Liu, Yanhong1,2 ; Song, Shouan 1,2; Yang, Lei1,2 ; Bian, Guibin1,3 ; Yu, Hongnian 1,4
发表期刊	JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
ISSN	1047-3203
	2022-02-01
卷号	83 页码:11
通讯作者	Yang, Lei(leiyang2019@zzu.edu.cn)
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.
关键词	Dynamic gesture understanding Transfer learning Feature fusion Dempster-Shafer evidence theory Support vector machine
DOI	10.1016/j.jvcir.2022.103454
关键词[WOS]	RECOGNITION ; EXTRACTION ; MATRIX ; SHAPE
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering
WOS记录号	WOS:000819856800004
出版者	ACADEMIC PRESS INC ELSEVIER SCIENCE
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49211
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Yang, Lei
作者单位	1.Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Henan, Peoples R China 2.Robot Percept & Control Engn Lab Henan Prov, Zhengzhou 450001, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.Edinburgh Napier Univ, Built Environm, Edinburgh EH10 5DT, Midlothian, Scotland
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.