期刊文献+

基于手语表达内容与表达特征的手语识别技术综述 被引量:2

A Survey of Sign Language Recognition Technology Based on Sign Language Expression Content and Expression Characteristics
下载PDF
导出
摘要 手语识别(SLR)技术是打破听障人群与健听人群间交流壁垒的重要技术手段。该文综述了近几年的手语数据集、评价指标以及手语识别方法。首先,系统梳理了手语数据集并分析了手语识别方法的数据集发展方向。其次,详细介绍了手语识别方法的评价指标。然后,根据手语表达内容、手语识别方法所采用的特征分类总结分析了孤立词手语识别方法与连续语句识别方法、仅依靠手部特征的手语识别方法与多特征融合的手语识别方法。最后探讨了手语识别技术面临的挑战及其发展方向。 Sign Language Recognition(SLR)technology is an important technical means to break the communication barrier between hearing-impaired people and healthy people.The sign language datasets,evaluation indicators and sign language recognition methods in recent years are summarized.Firstly,the sign language dataset is systematically summarized and the development trend of the dataset of sign language recognition methods is analyzed.Secondly,the evaluation indicator of sign language recognition method is introduced in detail.Then,according to the content of sign language expression and the features used in sign language recognition methods,isolated word sign language recognition methods and continuous sign language recognition methods,sign language recognition methods relying only on hand features and sign language recognition methods of multi feature fusion are summarized and analyzed.Finally,the challenges and development direction of sign language recognition technology are discussed.
作者 陶唐飞 刘天宇 TAO Tangfei;LIU Tianyu(Key Laboratory of Education Ministry for Modern Design&Rotor-Bearing System,Xi’an 710049,China;School of Mechanical Engineering,Xi’an Jiaotong University,Xi’an 710049,China)
出处 《电子与信息学报》 EI CSCD 北大核心 2023年第10期3439-3457,共19页 Journal of Electronics & Information Technology
基金 陕西省重点研发计划(2020KWZ-003)。
关键词 手语识别技术 手语数据集 孤立词手语识别 连续手语识别 多特征融合手语识别 Sign language Recognition Technique(SLR) Sign language dataset Isolated sign language recognition Continuous sign language recognition Multi feature fusion sign language recognition
  • 相关文献

参考文献4

二级参考文献22

  • 1张良国,高文,陈熙霖,陈益强,王春立.面向中等词汇量的中国手语视觉识别系统[J].计算机研究与发展,2006,43(3):476-482. 被引量:11
  • 2Ong S C W, Ranganath S. Automatic sign language analysis : A survey and the future beyond lexical meaning. IEEE Transactions on Pattern Analysis Machine Intelligence, 2005, 27(6) : 873-891
  • 3Starner T, Pentland A. Visual recognition of American sign language using hidden Markov models//Proceedings of the International Conference on Automatic Face and Gesture Recognition. Zurich, Switzerland, 1995:189-194
  • 4Bauer B, Hienz H. Relevant features for video-based continuous sign language recognition//Proceedings of the International Conference on Automatic Face and Gesture Recognition. Grenoble, France, 2000:440-445
  • 5Bowden R, Windridge D, Kadir T, Zisserman A, Brady M.A linguistic feature vector for the visual interpretation of sign language//Proceedings of the European Conference on Computer Vision. Prague, Czech Republic, 2004:390-401
  • 6Vogler C, Metaxas D. ASL recognition based on a coupling between hmms and 3D motion analysis//Proceedings of the IEEE International Conference on Computer Vision. Bombay, India, 1998:363-369
  • 7Wu Y, Huang T S. View-independent recognition of hand postures//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head, SC, USA, 2000, 2:88-94
  • 8Rao C, Gritai A, Shah M, Syeda-Mahmood T. View invariant alignment and matching of video sequences//Proceedings of the IEEE International Conference on Computer Vision. Nice, France, 2003:939-945
  • 9Wang Q, Chen X, Zhang L, Wang C, Gao W. Viewpoint invariant sign language recognition. Computer Vision and Image Understanding, 2007, 108(1-2): 87-97
  • 10Fischler M A, Bolles R C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 1981, 24(6) : 381-395

共引文献39

同被引文献5

引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部