CASIA OpenIR
Dynamic hand gesture recognition using motion pattern and shape descriptors
Xing, Meng1; Hu, Jing1; Feng, Zhiyong2; Su, Yong1; Peng, Weilong1; Zheng, Jinqing3
Source PublicationMULTIMEDIA TOOLS AND APPLICATIONS
ISSN1380-7501
2019-04-01
Volume78Issue:8Pages:10649-10672
Corresponding AuthorFeng, Zhiyong(zyfeng@tju.edu.cn)
AbstractThe 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.
KeywordDynamic hand gesture recognition Hand configuration Spatial-temporal variability Motion pattern descriptor Shape descriptor
DOI10.1007/s11042-018-6553-9
WOS KeywordDEPTH ; HISTOGRAMS ; GRADIENTS
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000467495400050
PublisherSPRINGER
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24227
Collection中国科学院自动化研究所
Corresponding AuthorFeng, Zhiyong
Affiliation1.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
Recommended Citation
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.
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