张群,
李辉
青岛科技大学信息科学技术学院 青岛 266061
基金项目:国家自然科学基金(61702295, 61672305)，山东省重点研发计划项目(2017GGX10127)
详细信息
作者简介:张淑军：女，1980年生，副教授，研究方向为计算机视觉
张群：女，1994年生，硕士生，研究方向为计算机视觉
李辉：男，1984年生，副教授，研究方向为计算机视觉
通讯作者:张淑军 lindazsj@163.com
中图分类号:TP391计量
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被引次数:0
出版历程
收稿日期:2019-06-06
修回日期:2019-11-20
网络出版日期:2020-01-18
刊出日期:2020-06-04
Review of Sign Language Recognition Based on Deep Learning
Shujun ZHANG,,Qun ZHANG,
Hui LI
College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
Funds:The National Natural Science Foundation of China (61702295, 61672305), The Key Research & Development Plan Project of Shandong Province (2017GGX10127)
摘要
摘要:手语识别涉及计算机视觉、模式识别、人机交互等领域，具有重要的研究意义与应用价值。深度学习技术的蓬勃发展为更加精准、实时的手语识别带来了新的机遇。该文综述了近年来基于深度学习的手语识别技术，从孤立词与连续语句两个分支展开详细的算法阐述与分析。孤立词识别技术划分为基于卷积神经网络(CNN)、3维卷积神经网络(3D-CNN)和循环神经网络(RNN) 3种架构的方法；连续语句识别所用模型复杂度更高，通常需要辅助某种长时时序建模算法，按其主体结构分为双向长短时记忆网络模型、3维卷积网络模型和混合模型。归纳总结了目前国内外常用手语数据集，探讨了手语识别技术的研究挑战与发展趋势，高精度前提下的鲁棒性和实用化仍有待于推进。
关键词:深度学习/
手语识别/
卷积网络/
循环神经网络/
长时序建模
Abstract:Sign language recognition involves computer vision, pattern recognition, human-computer interaction, etc. It has important research significance and application value. The flourishing of deep learning technology brings new opportunities for more accurate and real-time sign language recognition. This paper reviews the sign language recognition technology based on deep learning in recent years, formulates and analyzes the algorithms from two branches - isolated words and continuous sentences. The isolated-word recognition technology is divided into three structures: Convolutional Neural Network (CNN), Three-Dimensional Convolutional Neural Network (3D-CNN) and Recurrent Neural Network (RNN) based method. The model used for continuous sentence recognition has higher complexity and is usually assisted with certain kind of long-term temporal sequence modeling algorithm. According to the major structure, there are three categories: the bidirectional LSTM, the 3D convolutional network model and the hybrid model. Common sign language datasets at home and abroad are summarized. Finally, the research challenges and development trends of sign language recognition technology are discussed, concluding that the robustness and practicality on the premise of high-precision still requires to be promoted.
Key words:Deep learning/
Sign language recognition/
Convolutional Neural Network (CNN)/
Recurrent Neural Network (RNN)/
Long-term temporal sequence modeling
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