步态识别现状及发展趋势

doi:10.11959/j.issn.1000-0801.2020189

Abstract

Abstract:

Gait recognition is an advanced technology in the field of biometric identification.In view of the advantages of long-distance identification,uncontrolled identification and difficult camouflage,it has great potential to promote the development of pattern recognition,image processing,treatment and recovery.Firstly,the key technologies,research status and the common databases of gait recognition were introduced.Then,its application prospect was analyzed.Finally,the development trend of gait recognition was judged.

Key words: gait recognition, biometric identification, advanced technology, pattern recognition, image processing

CLC Number:

TP391

Yingzhao ZHU,Man LI. Current situation and development trend of gait recognition[J]. Telecommunications Science, 2020, 36(8): 130-138.

Figures/Tables 7

References 51

[1]	ILIADIS M , WANG H , MOLINA R ,et al. Robust and low-rank representation for fast face identification with occlusions[J]. IEEE Transactions on Image Processing, 2017,26(5): 2203-2218.
[2]	HARIYANTO , SUDIRO S A , LUKMAN S . Minutiae matching algorithm using artificial neural network for fingerprint recognition[C]// Proceedings of 2015 Third International Conference on Artificial International Conference on Artificial Intelligence. Piscataway:IEEE Press, 2015: 37-41.
[3]	VYAS R , KANUMURI T , SHEORAN G . Iris recognition using 2-D gabor filter and XOR-SUM code[C]// Proceedings of 2016 1st India International Conference on Information Processing. Piscataway:IEEE Press, 2016: 1-5.
[4]	WHITTLE M W . Clinical gait analysis:a review[J]. Human Movement Science, 1996,15(3): 369-387.
[5]	王科俊, 丁欣楠, 邢向磊 ,等. 多视角步态识别综述[J]. 自动化学报, 2019,45(5): 841-852.
	WANG K J , DING X N , XING X L ,et al. A survey of multi-view gait recognition[J]. Acta Automatica Sinica, 2019,45(5): 841-852.
[6]	LIPTON A J , FUJIYOSHI H , PATIL R S . Moving target classification and tracking from real-time video[C]// Proceedings of Fourth IEEE Workshop on Applications of Computer Vision. Piscataway:IEEE Press, 1998: 8-14.
[7]	COLLINS R . A system for video surveillance and monitoring[J]. Vsam Final Report Carnegie Mellon University Technical Report, 2000,59(5): 329-337.
[8]	商磊, 张宇, 李平 . 基于密集光流的步态识别[J]. 大连理工大学学报, 2016,56(2): 214-220.
	SHANG L , ZHANG Y , LI P . Dense optical flow-based gait recognition[J]. Journal of Dalian University of Technology, 2016,56(2): 214-220.
[9]	胡帅 . 复杂场景下步态识别技术研究[D]. 哈尔滨:哈尔滨工程大学, 2013.
	HU S . Research on technology of gait identification in complex scenes[D]. Harbin:Harbin Engineering University, 2013.
[10]	WANG L , TAN T , NING H Z ,et al. Silhouette analysis-based gait recognition for human identification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003,25(12): 1505-1518.
[11]	王竣 . 基于步态的身份识别研究[D]. 杭州:中国计量大学, 2017.
	WANG J . Research on gait-based human identification[D]. Hangzhou:China Jiliang University, 2017.
[12]	寇向阳 . 步态行为分析与比对方法研究[D]. 北京:中国人民公安大学, 2017.
	KOU X Y . Research on gait behavior analysis and alignment[D]. Beijing:People’s Public Security University of China, 2017.
[13]	薛傲 . 基于特征融合的步态识别算法研究[J]. 电脑知识与技术, 2018,14(25): 216-218.
	XUE A . Research on gait recognition algorithm based on feature fusion[J]. Computer Knowledge and Technology, 2018,14(25): 216-218.
[14]	KRIZHEVSKY A , SUTSKEVER I , HINTON G E . Imagenet classification with deep convolutional neural networks[C]// Proceedings of 26th Annual Conference on Neural Information Processing Systems. Cambridge:MIT Press, 2012: 1097-1105.
[15]	李凯, 曹可凡 . 受限玻尔兹曼机的步态特征提取及其识别[J]. 河北大学学报(自然科学版), 2019,39(6): 657-665.
	LI K , CAO K F . Gait feature extraction based on restricted Boltzmann machine and its recognition method[J]. Journal of Hebei University (Natural Science Edition), 2019,39(6): 657-665.
[16]	LEE L , GRIMSON W E L . Gait analysis for recognition and classification[C]// Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition. Piscataway:IEEE Press, 2002: 148-155.
[17]	DENG M , WANG C , CHENG F ,et al. Fusion of spatial-temporal and kinematic features for gait recognition with deterministic learning[J]. Pattern Recognition, 2017,67(2): 186-200.
[18]	KRZESZOWSKI T , MICHALCZUK A , KWOLEK B ,et al. Gait recognition based on marker-less 3D motion capture[C]// Proceedings of 10th IEEE International Conference on Advanced Video and Signal Based. Piscataway:IEEE Press, 2013: 232-237.
[19]	TANG J , LUO J , TJAHJADI T ,et al. Robust arbitrary-view gait recognition based on 3D partial similarity matching[J]. IEEE Transactions on Image Processing, 2017,26(1): 7-22.
[20]	HAN J , BHANU B . Individual recognition using gait energy image[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006,28(2): 316-322.
[21]	RIDA I , BOUBCHIR L , ALMAADEED N ,et al. Robust model-free gait recognition by statistical dependency feature selection and globality-locality preserving projections[C]// Proceedings of 39th International Conference on Telecommunications and Signal Processing. Piscataway:IEEE Press, 2016: 652-655.
[22]	ZENG W , WANG C , YANG F . Silhouette-based gait recognition via deterministic learning[J]. Pattern Recognition, 2014,47(11): 3568-3584.
[23]	刚域森 . 基于步态的行人识别研究与应用[D]. 徐州:中国矿业大学, 2019.
	GANG Y S . Study and application of pedestrian recognition based on gait[D]. Xuzhou:China University of Mining and Technology, 2019.
[24]	WANG C , ZHANG J , PU J ,et al. Chrono-gait image:a novel temporal template for gait recognition[C]// Proceedings of European Conference on Computer Vision. Berlin:Springer Press, 2010: 257-270.
[25]	LAM T H W , CHEUNG K H , LIU J N K . Gait flow image:a silhouette-based gait representation for human identification[J]. Pattern Recognition, 2011,44(4): 973-987.
[26]	余涛 . 基于深度学习的人体步态识别算法研究[D]. 成都:电子科技大学, 2018.
	YU T . Research on human gait recognition algorithm based on deep learning[D]. Chengdu:University of Electronic Science and Technology of China, 2018.
[27]	WU Z , HUANG Y , WANG L ,et al. A comprehensive study on cross-view gait based human identification with deep CNN[J]. IEEE Transactions on Pattern Analysisand Machine Intelligence, 2017,39(2): 209-226.
[28]	YU S , CHEN H , WANG Q ,et al. Invariant feature extraction for gait recognition using only one uniform model[J]. Neurocomputing, 2017,39(5): 81-93.
[29]	YU S , CHEN H , REYES E B ,et al. GaitGAN:invariant gait feature extraction using generative adversarial networks[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2017: 30-37.
[30]	CHAO H , HE Y , ZHANG J ,et al. GaitSet:regarding gait as a set for cross-view gait recognition[C]// Proceedings of the National Conference on Artificial Intelligence. California:AAAI Press, 2019: 8126-8133.
[31]	ZHANG K , LUO W , MA L ,et al. Learning joint gait representation via quintuplet loss minimization[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2019: 4700-4709.
[32]	ZHANG Z , TRAN L , YIN X ,et al. Gait recognition via disentangled representation learning[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2019: 4710-4719.
[33]	YU S Q , TAN D L , HUANG K Q ,et al. Reducing the effect of noise on human contour in gait recognition[C]// Proceedings of International Conference on Biometrics. Piscataway:IEEE Press, 2007: 338-346.
[34]	TARIQ M , SHAH M A . Review of model-free gait recognition in biometrie systems[C]// Proceedings of 23rd International Conference on Automation and Computing. Piscataway:IEEE Press, 2017: 1-7.
[35]	CHALIDABHONGSE T , KRUGER V , CHELAPPA R . The UMD database for human identification at a distance[R]. Maryland:University of Maryland, 2001.
[36]	TANAWONGSUWAN R , BOBICK A . Performance analysis of time distance gait parameters under different speeds[C]// Proceedings of 4th International Conference on Audio and Video-Baised Biometric Person Authentication. Berlin:Springer Press, 2003: 715-724.
[37]	CUNTOOR N , KALE A , CHELAPPA R . Combining multiple evidences for gait recognition[C]// Proceedings of IEEE International Conference on Acoustics,Speech,and Signal Processing. Piscataway:IEEE Press, 2003: 33-36.
[38]	SEELY R D , SAM A S , LEE M ,et al. The university of southampton multi-biometfic tunnel and introducing a novel 3D gait dataset[C]// Proceedings of IEEE International Conference on Biometrics:Theory,Applications and Systems. Piscataway:IEEE Press, 2008: 1-6.
[39]	GROSS R , SHI J . The CMU motion of body(MoBo) database[DB].2001. 2001.
[40]	陈峰 . 基于步态的目标识别技术的研究[D]. 北京:北京交通大学, 2018.
	CHEN F . Research on target recognition technique based on gait[D]. Beijing:Beijing Jiaotong University, 2018.
[41]	殷赫 . 步态识别：人工智能“慧眼”[J]. 上海信息化, 2019(11): 61-63.
	YIN H . Gait recognition:artificial intelligence “eyes”[J]. Shanghai Informatization, 2019(11): 61-63.
[42]	王申涛, 陈晓辉 . 基于长短时记忆网络（LSTM）的帕金森步态识别[J]. 信息通信, 2020(1): 13-15.
	WANG S T , CHEN X H . Detection of freezing of gait for parkinson’s disease patients based on LSTM network[J]. Information ＆ Communications, 2020(1): 13-15.
[43]	周江南 . 步态识别让空调“知冷知热”[J]. 大众用电, 2019,34(9):50.
	ZHOU J N . Gait recognition allows air conditioners to “know cold and heat”[J]. Public Electricity, 2019,34(9):50.
[44]	金堃, 陈少昌 . 步态识别现状与发展[J]. 计算机科学, 2019,46(S1): 30-34.
	JIN K , CHEN S C . Status and development of gait recognition[J]. Computer Science, 2019,46(S1): 30-34.
[45]	闫李倩 . 基于卷积神经网络步态识别方法的研究[D]. 西安:西安科技大学, 2019.
	YAN L Q . Research on gait recognition method based on convolutional neural network[D]. Xi’an:Xi’an University of Science and Technology, 2019.
[46]	GOODFELLOW I J , POUGET-ABADIE J , MIRZA M ,et al. Generative adversarial nets[C]// Proceedings of International Conference on Neural Information Processing Systems. Cambridge:MIT Press, 2014: 2672-2680.
[47]	KINGMA D , WELLING M . Auto-encoding variational bayes[C]// Proceedings of 2nd International Conference on Learning Representations. California:AAAI Press, 2014: 1-14.
[48]	KINGMA D , DHARIWAL P . Glow:generative flow with invertible 1×1 convolutions[C]// Proceedings of 32nd Annual Conference on Neural Information Processing Systems. Cambridge:MIT Press, 2018: 10215-10224.
[49]	李玲玲 . 人脸识别和步态识别技术融合的必要性[J]. 电脑知识与技术, 2018,14(28): 187-188.
	LI L L . Necessity of integration of face recognition and gait recognition technology[J]. Computer Knowledge and Technology, 2018,14(28): 187-188.
[50]	马小莉 . 生物识别技术的类别及应用[J]. 技术与市场, 2020,27(3): 51-52.
	MA X L . Types and applications of biometrics[J]. Technology and Market, 2020,27(3): 51-52.
[51]	PUNYANI P , GUPTA R , KUMAR A . Human age-estimation system based on double-level feature fusion of face and gait images[J]. International Journal of Image and Data Fusion, 2018,9(1-4): 222-236.

Metrics

Recommended 0

No Suggested Reading articles found!

算法	优势	不足
基于单一轮廓特征	原理简单，容易实现	手工提取，识别准确性和稳健性差
基于融合特征	克服单一特征不稳定性	手工提取，增加特征分析时间
基于深度学习	自动提取，提取的特征最为完整	参数量大，训练占用内存多

数据集	人数	序列	影响因素
CASIA-A^[10]	20	240	3个视角
CASIA-B^[33]	124	13 640	11个视角，着装，携带物品
USF^[34]	122	1 870	2 个视角，路面，鞋子，携带物品
CMU Mobo^[35]	25	600	6 个视角，速度，携带物，倾斜路面
MIT AI^[36]	24	194	视角、时间
Georgia Tech^[37]	20	188	视角、时间、距离
USMT^[38]	103	1 005	多特征、三维
HID-UMD-1^[39]	25	100	4个视角
HID-UMD-2^[39]	55	220	2个视角

Current situation and development trend of gait recognition

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 51

Related Articles 2

Metrics

Recommended 0

[1]	Hui Cheng,Jinglong Fang,Daquan Wang,Xingqi Wang. Performance Analysis of Simplification of Hyperplane Support Vector Machine [J]. Telecommunications Science, 2015, 31(8): 78-83.
[2]	Lei Ye,Qingzang Huang,Mingyuan Yu,Donghui Yu. Integration of Medical Information Based on Cloud Computing [J]. Telecommunications Science, 2011, 27(12): 12-16.