Dynamic hand gesture recognition using motion pattern and shape descriptors

doi:10.1007/s11042-018-6553-9

	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
刊名	MULTIMEDIA TOOLS AND APPLICATIONS
	2019-04-01
卷号	78 期号:8 页码:10649-10672
关键词	Dynamic hand gesture recognition Hand configuration Spatial-temporal variability Motion pattern descriptor Shape descriptor
ISSN号	1380-7501
DOI	10.1007/s11042-018-6553-9
通讯作者	Feng, Zhiyong(zyfeng@tju.edu.cn)
英文摘要	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.
WOS关键词	DEPTH ; HISTOGRAMS ; GRADIENTS
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000467495400050
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/24226]
专题	中国科学院自动化研究所
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.