Dynamic hand gesture recognition using motion pattern and shape descriptors

doi:10.1007/s11042-018-6553-9

CASIA OpenIR > 中科院工业视觉智能装备工程实验室 > 精密感知与控制

	Dynamic hand gesture recognition using motion pattern and shape descriptors
	Xing, Meng 1; Hu, Jing 1; Feng, Zhiyong 2; Su, Yong 1; Peng, Weilong 1; Zheng, Jinqing 3
发表期刊	MULTIMEDIA TOOLS AND APPLICATIONS
ISSN	1380-7501
	2019-04-01
卷号	78 期号:8 页码:10649-10672
通讯作者	Feng, Zhiyong(zyfeng@tju.edu.cn)
摘要	The key problems of dynamic hand gesture recognition are large intra-class (gesture types, without considering hand configuration) spatial-temporal variability and similar inter-class (gesture types, only considering hand configuration) motion pattern. Firstly, for intra-class spatial-temporal variability, the key is to reduce the spatial-temporal variability. Due to the average operation can improve the robustness very well, we propose a motion pattern descriptor, Time-Wise Histograms of Oriented Gradients (TWHOG), which extracts the average spatial-temporal information in the space-time domain from three orthogonal projection views (XY, YT, XT). Secondly, for similar inter-class motion pattern, accurate representation of hand configuration is especially important. Therefore, the difference in detail needs to be fully captured, and the shape descriptor can amplify subtle differences. Specifically, we introduce Depth Motion Maps-based Histograms of Oriented Gradients (DMM-HOG) to capture subtle differences in hand configurations between different types of gestures with similar motion patterns. Finally, we concatenate TWHOG and DMM-HOG to form the final feature vector Time-Shape Histograms of Oriented Gradients (TSHOG) and verify the effectiveness of the connection from quantitative and qualitative perspective. Comparison study with the state-of-the-art approaches are conducted on two challenge depth gesture datasets (MSRGesture3D, SKIG). The experiment result shows that TSHOG can achieve satisfactory performance while keeping a relative simple model with lower complexity as well as higher generality.
关键词	Dynamic hand gesture recognition Hand configuration Spatial-temporal variability Motion pattern descriptor Shape descriptor
DOI	10.1007/s11042-018-6553-9
关键词[WOS]	DEPTH ; HISTOGRAMS ; GRADIENTS
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000467495400050
出版者	SPRINGER
引用统计	被引频次：5[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/173211/24227
专题	中科院工业视觉智能装备工程实验室_精密感知与控制
通讯作者	Feng, Zhiyong
作者单位	1.Tianjin Univ, Sch Comp Sci & Technol, Tianjin 300072, Peoples R China 2.Tianjin Univ, Sch Comp Software, Tianjin 300072, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Xing, Meng,Hu, Jing,Feng, Zhiyong,et al. Dynamic hand gesture recognition using motion pattern and shape descriptors[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2019,78(8):10649-10672.
APA	Xing, Meng,Hu, Jing,Feng, Zhiyong,Su, Yong,Peng, Weilong,&Zheng, Jinqing.(2019).Dynamic hand gesture recognition using motion pattern and shape descriptors.MULTIMEDIA TOOLS AND APPLICATIONS,78(8),10649-10672.
MLA	Xing, Meng,et al."Dynamic hand gesture recognition using motion pattern and shape descriptors".MULTIMEDIA TOOLS AND APPLICATIONS 78.8(2019):10649-10672.