人体步态识别方法与技术

doi:10.13229/j.cnki.jdxbgxb20190264

Abstract

Abstract:

Gait recognition is a hotspot in the field of pattern recognition, information security and clinical medicine in recent years. This paper mainly reviewed the four aspects of the gait data acquisition instruments, common gait databases, gait feature extraction and the methods of gait recognition. Firstly, the advantages and disadvantages, reliability, and application scenarios of the commonly used gait data acquisition instruments were introduced. Secondly, the common gait data sets were compared and analyzed from six aspects of the establishment of institutions, the sample size, the sampling rate, the environments, the instruments and the variables. Third, the existing gait parameter extraction methods were divided into model-based methods and non-model-based methods to elaborate in detail. Fourth, the gait recognition was introduced from support vector machine, auto-encoder and convolutional neural network, respectively, and the above methods from the two application directions of personal identification and abnormal gait identification were compared respectively. Finally, the shortcomings of current research and the future development directions were pointed out based on practical application.

Key words: artificial intelligence, gait data acquisition, gait database, gait feature extraction, gait recognition

CLC Number:

TP18

Yi-bin LI,Jia-min GUO,Qin ZHANG. Methods and technologies of human gait recognition[J].Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 1-18.

Figures/Tables 21

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Table 1

Fig.11

Fig.12

Fig.13

Fig.14

Fig.15

Fig.16

Fig.17

Fig.18

Table 2

Table 3

References 105

1	Cao Y, Li B Z, Li Q N, et al. Kinect‐based gait analyses of patients with Parkinson's disease, patients with stroke with hemiplegia, and healthy adults[J]. CNS Neuroscience & Therapeutics, 2017, 23(5): 447-449.
2	Eltoukhy M, Kuenze C, Oh J, et al. Microsoft Kinect can distinguish differences in over-ground gait between older persons with and without Parkinson's disease[J]. Medical Engineering & Physics, 2017, 44: 1-7.
3	Wang T, Wang Z, Zhang D, et al. Recognizing Parkinsonian gait pattern by exploiting fine-grained movement function features[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2016, 8(1): 1-22.
4	Mileti I, Germanotta M, Di Sipio E, et al. Measuring Gait Quality in Parkinson’s Disease through Real-Time Gait Phase Recognition[J]. Sensors, 2018, 18(3): 919.
5	Cui C, Bian G B, Hou Z G, et al. Simultaneous recognition and assessment of post-stroke hemiparetic gait by fusing kinematic, kinetic, and electrophysiological data[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(4): 856-864.
6	Benson L C, Clermont C A, Bošnjak E, et al. The use of wearable devices for walking and running gait analysis outside of the lab: a systematic review[J]. Gait & Posture, 2018, 63: 124-138.
7	Simon S R. Quantification of human motion: gait analysis—benefits and limitations to its application to clinical problems[J]. Journal of Biomechanics, 2004, 37(12): 1869-1880.
8	Figueiredo J, Santos C P, Moreno J C. Automatic recognition of gait patterns in human motor disorders using machine learning: a review[J]. Medical Engineering & Physics, 2018，53：1-12.
9	Huang P S, Harris C J, Nixon M S. Recognising humans by gait via parametric canonical space[J]. Artificial Intelligence in Engineering, 1999, 13(4): 359-366.
10	Collins R T, Gross R, Shi J. Silhouette-based human identification from body shape and gait[C]∥Proceedings of Fifth IEEE International Conference on. Automatic Face and Gesture Recognition, Washington, DC, USA, 2002: 366-371.
11	Han J, Bhanu B. Individual recognition using gait energy image[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2006(2): 316-322.
12	Lam T H W, Lee R S T. A new representation for human gait recognition: motion silhouettes image (MSI)[C]∥International Conference on Biometrics. Berlin: Springer, 2006: 612-618.
13	Prakash C, Kumar R, Mittal N. Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges[J]. Artificial Intelligence Review, 2018, 49(1): 1-40.
14	Yang C C, Yeh-Liang H. A review of accelerometry-based wearable motion detectors for physical activity monitoring[J]. Sensors, 2010, 10(8): 7772-7788.
15	埃里克·F·布赖恩. 用加速度计感测转向轴倾斜角和外倾角[P]. 中国：1768246 A,2006-08-17.
16	Büsching F, Kulau U, Gietzelt M, et al. Comparison and validation of capacitive accelerometers for health care applications[J]. Computer Methods & Programs in Biomedicine, 2012, 106(2): 79-88.
17	Bouten C V C, Koekkoek K T M, Verduin M, et al. A triaxial aceler-ometer and portable data processing unit for the assessment of daily physical activity[J]. IEEE Transactions on Bio-Medical Engineering, 1997, 44(3):136-147.
18	Godfrey A, Del D S, Barry G, et al. Within trial validation and reliability of a single tri-axial accelerometer for gait assessment[C]∥The 36th Annual International Conference of IEEE Engineering in Medicine and Biology Society,Chicago,IL,USA，2014:5892-5895.
19	Din S D, Godfrey A, Rochester L. Validation of an accelerometer to quantify a comprehensive battery of gait characteristics in healthy older adults and Parkinson's disease: toward clinical and at home use[J]. IEEE J Biomed Health Inform, 2016, 20(3):838-847.
20	Saunders J B, Inman V T, Eberhart H D. The major determinants in normal and pathological gait[J]. The Journal of Bone and Joint Surgery, 1953, 35(3): 543-558.
21	Williams G, Doughty K, Cameron K, et al. A smart fall and activity monitor for telecare applications[C]∥Proceedings of the of International Conference on Engineering in Medicine and Biology Society, Montreal, Que, Canada, 1998:1151-1154.
22	Weijer R, Hoozemans M, Van J D, et al. Self-perceived gait stability modulates the effect of daily life gait quality on prospective falls in older adults[J]. Gait & Posture, 2018，62:475-479.
23	Steeves J A, Tyo B, Connolly C, et al. Validity and Reliability of the Omron HJ-303 GoSmart tri-axis accelerometer-based pedometer: 2023[J]. Medicine & Science in Sports & Exercise, 2010, 42(7):1014-1020.
24	Sotoperezdecelis E, Kim H, Rojocastillo M P, et al. A pilot study of an accelerometer-equipped smartphone to monitor older adults with cancer receiving chemotherapy in Mexico[J]. Journal of Geriatric Oncology, 2017, 9(2):145-151.
25	侯向锋, 刘蓉, 周兆丰. 加速度传感器MMA7260在步态特征提取中的应用[J]. 传感技术学报, 2007, 20(3):507-511.
	Hou Xiang-feng, Liu Rong, Zhou Zhao-feng. Application of accelerometer MMA7260 in the gait feature extraction[J]. Chinese Journal of Sensors and Actuators, 2007, 20(3):507-511.
26	刘蓉, 黄璐, 李少伟,等. 基于步态加速度的步态分析研究[J]. 传感技术学报, 2009, 22(6):893-896.
	Liu Rong, Huang Lu, Li Shao-wei, et al. Gait analysis based on gait acceleration[J]. Chinese Journal of Sensors and Actuators, 2009, 22(6):893-896.
27	Zhang Y, Pan G, Jia K, et al. Accelerometer-based gait recognition by sparse representation of signature points with clusters[J]. IEEE Transactions on Cybernetics, 2014, 45(9):1864-1875.
28	Medda A, Vaughan A, Liu B, et al. Activity recognition using statistical gait parameters from a single accelerometer[C]∥Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA,2014:189-193.
29	Manupibul U, Charoensuk W, Kaimuk P. Design and development of SMART insole system for plantar pressure measurement in imbalance human body and heavy activities[C]∥International Conference on Biomedical Engineering and Biotechnology, Shanghai,China, 2015:1-5.
30	Kong K, Tomizuka M. A gait monitoring system based on air pressure sensors embedded in a shoe[J]. IEEE/ASME Transactions on Mechatronics, 2009, 14(3):358-370.
31	Redd C B, Bamberg S J M. A wireless sensory feedback device for real-time gait feedback and training[J]. IEEE/ASME Transactions on Mechatronics, 2012, 17(3):425-433.
32	Lin F, Wang A, Zhuang Y, et al. Smart insole: a wearable sensor device for unobtrusive gait monitoring in daily life[J]. IEEE Transactions on Industrial Informatics, 2016, 12(6): 2281-2291.
33	Creaby M W, May K, Bennell K L. Insole effects on impact loading during walking[J]. Ergonomics, 2011, 54(7): 665-671.
34	Chen D, Cai Y, Huang M C. Customizable pressure sensor array: design and evaluation[J]. IEEE Sensors Journal, 2018, 18(15):6337-6344.
35	Qi Y, Soh C B, Gunawan E, et al. Ambulatory measurement of three-dimensional foot displacement during treadmill walking using wearable wireless ultrasonic sensor network[J]. IEEE Journal of Biomedical & Health Informatics, 2015, 19(2): 446-452.
36	Qi Y, Soh C B, Gunawan E, et al. Assessment of foot trajectory for human gait phase detection using wireless ultrasonic sensor network[J]. IEEE Transactions on Neural Systems & Rehabilitation Engineering, 2016, 24(1):88-97.
37	Wearing S C, Reed L, Hooper S L, et al. Running shoes increase achilles tendon load in walking: an acoustic propagation study[J]. Med Sci Sports Exerc, 2014, 46(8):1604-1609.
38	Wulf M, Wearing S C, Hooper S L, et al. achilles tendon loading patterns during barefoot walking and slow running on a treadmill: An ultrasonic propagation study[J]. Scand J Med Sci Sports, 2016, 25(6):868-875.
39	Little J, Boyd J. Recognizing people by their gait: the shape of motion[J]. Videre: Journal of Computer Vision Research, 1998, 1(2): 1-32.
40	Tanawongsuwan R, Bobick A. Gait recognition from time-normalized joint-angle trajectories in the walking plane[C]∥IEEE Computer Society Conference on Computer Vision & Pattern Recognition, Kauai, HI, USA,2001:726-731.
41	Park Steve, Amos Y, Daniel S, et al. Human identification at a distance at gatech[DB/OL][2018-8-25]. https:∥www.cc.gatech.edu/cpl/projects/hid/
42	Gross R, Shi J. The cmu motion of body (mobo) database[R]. Pittsburgh: Robotics Institute, Carnegie Mellon University, 2001.
43	Sarkar S, Phillips P J, Liu Z, et al. The humanid gait challenge problem: data sets, performance, and analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(2): 162-177.
44	Wang L, Tan T, Ning H, et al. Silhouette analysis-based gait recognition for human identification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(12): 1505-1518.
45	Yu S, Tan D, Tan T. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition[C]∥The 18th International Conference on Pattern Recognition, Hong Kong, China, 2006: 441-444.
46	Tan D, Huang K, Yu S, et al. Uniprojective features for gait recognition[C]∥International Conference on Biometrics. Berlin: Springer, 2007: 673-682.
47	Vicon Vantage Reference[M/OL]. [2019-02-28]. https:∥.
48	Go Further With Vicon MX T-Series[M/OL].[2019-02-28].https:∥www.vicon.com/downloads/documentation/go-further-with-vicon-mx-t-series，2018.
49	Pfister A, West A M, Bronner S, et al. Comparative abilities of Microsoft Kinect and Vicon 3D motion capture for gait analysis[J]. Journal of medical Engineering & Technology, 2014, 38(5): 274-280.
50	Eltoukhy M, Oh J, Kuenze C, et al. Improved kinect-based spatiotemporal and kinematic treadmill gait assessment[J]. Gait & Posture, 2017, 51: 77-83.
51	Eltoukhy M, Kuenze C, Oh J, et al. Microsoft Kinect can distinguish differences in over-ground gait between older persons with and without Parkinson's disease[J]. Medical Engineering &Physics, 2017, 44: 1-7.
52	Tanaka R, Takimoto H, Yamasaki T, et al. Validity of time series kinematical data as measured by a markerless motion capture system on a flatland for gait assessment[J]. Journal of Biomechanics, 2018, 71: 281-285.
53	Tanawongsuwan R, Bobick A. Gait recognition from time-normalized joint-angle trajectories in the walking plane[C]∥Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA, 2001.
54	Seely R D, Samangooei S, Middleton L, et al. The university of southampton multi-biometric tunnel and introducing a novel 3d gait dataset[C]∥IEEE Second International Conference on Biometrics: Theory, Applications and Systems，Arlington, VA, USA,2008.
55	Shutler J D, Grant M G, Nixon M S, et al. On a Large Sequence-Based Human Gait Database[M].Berlin: Springer, 2004:66-71.
56	Department of Intelligent Media, The Institute of Scientific and Industrial Research, University Osaka. OU-ISIR biometric database [DB/OL]. [2019-2-28]. http:∥www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitLP.html,2018.
57	Yu S, Wang Q, Huang Y. A large RGB-D gait dataset and the baseline algorithm[C]∥The 8th Chinese Conference on Biometric Recognition,Jinan,China,2013: 417-424.
58	Andersson V O, de Araújo R M. Person identification using anthropometric and gait data from kinect sensor[C]∥AAAI, Austin, Texas, USA, 2015: 425-431.
59	PhysioBank databases[DB/OL]. [2019-2-28]. https:∥physionet.org/physiobank/database/#gait.
60	Cunado D, Nixon M S, Carter J N. Using gait as a biometric, via phase-weighted magnitude spectra[C]∥LNCS, 1997, 1206: 93-102.
61	Okusa K, Kamakura T. Human gait modeling and statistical registration for the frontal view gait data with application to the normal/abnormal gait analysis[C]//IAENG Transactions on Engineering Technologies. Dordrecht:Springer, 2014: 525-539.
62	Guo Y, Xu G, Tsuji S. Understanding human motion patterns[C]∥Proceedings of the 12th IAPR International. Conference on Pattern Recognition, Jerusalem, Israel, 1994: 325-329.
63	Rohr K. Towards model-based recognition of human movements in image sequences[J]. CVGIP-Image Understanding, 1994, 59(1): 94-115.
64	Howe N R, Leventon M E, Freeman W T. Bayesian reconstruction of 3d human motion from single-camera video[C]∥Advances in Neural Information Processing Systems，Cambridge, MA, USA, 2000: 820-826.
65	Human Interface Guidelines[M/OL].[2019-3-3]. http: Springer,.
66	Gordon Rachel. Artificial intelligence senses people through walls[DB/OL]. [2019-2-28]. https:∥.
67	Sutherland D. The development of mature gait[J]. Gait & Posture, 1997, 6(2): 163-170.
68	Nguyen T N, Huynh H H, Meunier J. Skeleton-based abnormal gait detection[J]. Sensors, 2016, 16(11): 1792.
69	Glowinski S, Blazejewski A, Krzyzynski T. Human gait feature detection using inertial sensors wavelets[M]∥ Glowinski S，Blazejewski A，Krzyzynski T,ed. Wearable Robotics: Challenges and Trends, Springer, Cham, 2017: 397-401.
70	Cahill-Rowley K, Rose J. Temporal-spatial gait parameters and neurodevelopment in very-low-birth-weight preterm toddlers at 18-22 months[J]. Gait & Posture, 2016, 45: 83-89.
71	Ochi F, Esquenazi A, Hirai B, et al. Temporal—spatial feature of gait after traumatic brain injury[J]. The Journal of Head Trauma Rehabilitation, 1999, 14(2): 105-115.
72	Esquenazi A, Ofluoglu D, Hirai B, et al. The effect of an ankle-foot orthosis on temporal spatial parameters and asymmetry of gait in hemiparetic patients[J]. PM&R, 2009, 1(11): 1014-1018.
73	张勤, 李岳炀, 李贻斌,等. 基于Kinect的学步期幼儿自然步态特征提取[J]. 自动化学报, 2018, 44(6): 1096-1106.
	Zhang Qin, Li Yue-yang, Li Yi-bin, et al. Extraction of toddler's natural gait based on kinect[J]. Acta Automatica Sinica, 2018, 44(6):1096-1106.
74	Kamruzzaman J, Begg R K. Support vector machines and other pattern recognition approaches to the diagnosis of cerebral palsy gait[J]. IEEE Transactions on Biomedical Engineering, 2006, 53(12): 2479-2490.
75	Chen S W, Lin S H, Liao L D, et al. Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis[J]. Biomedical Engineering Online, 2011, 10(1): 99.
76	Qi Y, Soh C B, Gunawan E, et al. Estimation of spatial-temporal gait parameters using a low-cost ultrasonic motion analysis system[J]. Sensors, 2014, 14(8): 15434-15457.
77	Dempster W T, Gaughran G R L. Properties of body segments based on size and weight[J]. American Journal of Anatomy, 1967, 120(1): 33-54.
78	Ober D B, Neugebauer S P, Sallee P A. Training and feature-reduction techniques for human identification using anthropometry[C]∥Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), Washington, DC, USA, 2010: 1-8.
79	Johnson A Y, Bobick A F. A multi-view method for gait recognition using static body parameters[C]∥International Conference on Audio-and Video-Based Biometric Person Authentication, Washington, DC, US, 2001: 301-311.
80	贲晛烨, 徐森, 王科俊. 行人步态的特征表达及识别综述[J]. 模式识别与人工智能, 2012(1): 71-81.
	Xian-ye Ben, Xu Sen, Wang Ke-jun. Review on pedestrian gait feature expression and recognition[J]. Pattern Recognition and Artificial Intelligence, 2012(1): 71-81.
81	Wang C, Zhang J, Pu J, et al. Chrono-gait image: a novel temporal template for gait recognition[C]∥European Conference on Computer Vision, Heraklion, Crete, Greece, 2010: 257-270.
82	Ozen H, Boulgouris N V, Swash R. Gait recognition based on 3D holoscopic gait energy image[C]∥International Conference on 3D Immersion (IC3D), Brussels, Belgium,2017: 1-4.
83	He Y, Zhang J, Shan H, et al. Multi-task GANs for view-specific feature learning in gait recognition[J]. IEEE Transactions on Information Forensics and Security, 2019, 14(1): 102-113.
84	Lee H, Hong S, Nizami I F, et al. A noise robust gait representation: motion energy image[J]. International Journal of Control, Automation and Systems, 2009, 7(4): 638-643.
85	Nizami I F, Hong S, Lee H, et al. Automatic gait recognition based on probabilistic approach[J]. International Journal of Imaging Systems and Technology, 2010, 20(4): 400-408.
86	Liu Y, Collins R, Tsin Y. Gait sequence analysis using frieze patterns[C]∥European Conference on Computer Vision, Copenhagen, Denmark, 2002: 657-671.
87	Lee S, Liu Y, Collins R. Shape variation-based frieze pattern for robust gait recognition[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 2007: 1-8.
88	Bashir K, Xiang T, Gong S. Gait recognition using gait entropy image[C]∥The 3rd International Conference on Imaging for Crime Detection and Prevention, London ,UK, 2009.
89	Liu Z, Yang G. Use wavelet transform to gait recognition[C]∥International Congress on Image & Signal Processing, Shanghai, China, 2016.
90	Wang H, Fan Y, Fang B, et al. Generalized linear discriminant analysis based on Euclidean norm for gait recognition[J]. International Journal of Machine Learning & Cybernetics, 2018, 9(4):569-576.
91	Zhang H, Ye C. RGB-D Camera Based Walking Pattern Recognition by Support Vector Machines for a Smart Rollator[J]. International Journal of Intelligent Robotics & Applications, 2017, 1(1):32-41.
92	Wang X, Wang J, Yan K. Gait recognition based on Gabor wavelets and (2D) 2 PCA[J]. Multimedia Tools & Applications, 2017(3):1-17.
93	Yu S, Chen H, Wang Q, et al. Invariant feature extraction for gait recognition using only one uniform model[J]. Neurocomputing, 2017, 239(C):81-93.
94	Yeoh T W, Aguirre H E, Tanaka K. Stacked progressive auto-encoders for clothing-invariant gait recognition[C]∥International Conference on Computer Analysis of Images and Patterns, Ystad, Sweden, 2017: 151-161.
95	Wang Y, Chen Y, Bhuiyan M Z A, et al. Gait-based Human identification using acoustic sensor and deep neural network[J]. Future Generation Computer Systems, 2018, 86: 1228-1237.
96	Shiraga K, Makihara Y, Muramatsu D, et al. GEINet: view-invariant gait recognition using a convolutional neural network[C]∥International Conference on Biometrics, Halmstad, Sweden, 2016:1-8.
97	Chen Q, Wang Y, Liu Z, et al. Feature map pooling for cross-view gait recognition based on silhouette sequence images[C]//IEEE International Joint Conference on Biometrics (IJCB), Denver, CO, USA, 2017:54-61.
98	Wu Z, Huang Y, Wang L, et al. A comprehensive study on cross-view gait based human identification with deep CNNs[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(2):209-226.
99	Chen C, Liang J, Zhao H, et al. Gait recognition using hidden Markova model[C]∥International Conference on Natural Computation, Xi’an,China, 2006: 399-407.
100	Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]∥Advances in Neural Information Processing Systems, Montreal, QC, Canada, 2014: 2672-2680.
101	Yu S, Chen H, Reyes E B G, et al. Gait GAN: invariant gait feature extraction using generative adversarial networks[C]∥CVPR Workshops, Honolulu, HI, USA, 2017: 532-539.
102	Das D. Human gait classification using combined HMM & SVM hybrid classifier[C]∥International Conference on Electronic Design, Computer Networks & Automated Verification (EDCAV), Shillong, India, 2015: 169-174.
103	Takemura N, Makihara Y, Muramatsu D, et al. On input/output architectures for convolutional neural network-based cross-view gait recognition[J]. IEEE Transactions on Circuits and Systems for Video Technology，2019，29(9): 2708-2719.
104	Banaie M, Pooyan M, Mikaili M. Introduction and application of an automatic gait recognition method to diagnose movement disorders that arose of similar causes[J]. Expert Systems with Applications, 2011, 38(6): 7359-7363.
105	Pogorelc B, Bosnić Z, Gams M. Automatic recognition of gait-related health problems in the elderly using machine learning[J]. Multimedia Tools and Applications, 2012, 58(2): 333-354.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

建立机构	库名	样本容量	采样率/(帧?s^-1)	环境	仪器	变量
加州大学圣地亚哥分校	USDC^[39]	6人，42序列	30	室外	1台相机	位置
佐治亚理工大学	Georgia^[40,41]	20人，188序列	120	室内，室外	3D运动捕捉系统	距离、速度
佐治亚理工大学	Georgia ^[53]	18人，106序列和8人，49序列	30	室内	Ascension电磁动作捕捉系统	**
卡耐基梅隆大学	CMU Mobo^[42]	25人，600序列	30	室内	6台索尼DXC 9000	视角、衣着、速度、倾斜、抱球
南佛罗里达大学	USF^[43]	122人， 1 870序列	**	室外	2台相机、一个标定板	地面、衣着、时间
中国科学院自动化所	CASIA(A)^[44]	20人，240序列	25	室外	一台松下NV?DX100EN数码相机	视角
中国科学院自动化所	CASIA(B)^[45]	124人，1 364序列	25	室内	11台相机	视角、衣着
中国科学院自动化所	CASIA(C)^[46]	153人，1 530序列	25	室外(夜间)	红外(热感)相机	衣着、速度
南安普顿大学	USMT^[54]	103人，1 005序列	30	multi?biometric tunnel	8台Point Grey Dragonfly和1台IEEE1394	**
南安普顿大学	SOTON^[55]	超过100人，每人约8序列	25	室内，室外	3台佳能MV30i和3台索尼TRV900E PAL	视角
大阪大学	OU?ISIR Treadmill A^[56]	34人，408序列	60	室内	25台相机	速度
大阪大学	OU?ISIR Treadmill B^[56]	68人，1 350序列	60	室内	25台相机	服装
大阪大学	OU?ISIR Treadmill D^[56]	185人，370序列	60	室内	25台相机	步态波动
大阪大学	OU?ISIR LP^[56]	超过4 000人	30	室内	2台相机	视角
深圳大学	SZU RGB?D^[57]	99人，792序列	**	室内	ASUS Xtion PRO LIV?E、OpenNI SD?K	视角
佩洛塔斯联邦大学	Andresson^[58]	140人，每人500~600帧	30	室内	Kinect	**
PhysioNet	Gait Dynamics in Neuro?Degenerative Disease Data Base^[59]	64人，64份记录	**	室内	力敏电阻	疾病类型
PhysioNet	Gait in Parkinson’s Disease^[59]	166人	100	室内	压力传感器	帕金森

方法	输入特征	是否交叉视角	是否改变服装	条件	数据集	（平均）识别率/%
Multi?Task GANs^[83]	PEI	√	×	**	OU?ISIR	93.10
	PEI	√	×	**	CASIA(B)	74.60
	PEI	√	×	**	USF	94.70
SVM+ELDA^[90]	边界到质心的距离	√	×	**	CASIA(B)	97.50
SVM+ELDA^[90]	边界到质心的距离	√	×	**	SOTON	95.56
SVM+(2D)²PCA+Gabor^[92]	GEI	×	×	**	CASIA(B)	86.12
自编码器^[93]	GEI	√	×	**	CASIA(B)	63.72
	GEI	√	×	带包	CASIA(B)	40.38
	GEI	√	×	穿大衣	CASIA(B)	26.04
	GEI	√	×	**	SZU RGB?D	70.00
自编码器^[94]	GEI	×	√	**	OU?ISIR Treadmill	94.80
CNN^[96]	GEI	√	×	**	OU?ISIR LP	91.58
CNN^[97]	成对的步态轮廓序列	√	×	**	OU?ISIR LP	87.40
深度CNNs^[98]	GEI	√	×	**	CASIA(B)	84.67
	GEI	√	×	带包	CASIA(B)	90.77
	GEI	√	×	穿大衣	CASIA(B)	62.52
	GEI	√	×	**	OU?ISIR	92.78
	GEI	√	×	**	USF	96.70
HMM^[99]	滤波后的轮廓宽度信号	×	×	**	CMU Mobo	90.175
GAN^[101]	GEI	√	×	**	CASIA(B)	98.75
	GEI	√	×	带包	CASIA(B)	72.73
	GEI	√	×	穿大衣	CASIA(B)	41.50

方法	特征提取仪器	数据集	输入特征	异常步态类型	识别率(平均)/%
SVM^[5]	Qualisys运动捕捉系统、Bertec Corp、Mega Electronics Ltd	**	下肢关节轨迹、地面反作用力、肌电图	脑卒中	98.21
SVM+k?means^[91]	ASUS Xtion PRO LIVE	**	足、腿和踝关节的速度信号、踝关节的角度信号	膝伤	84.00
二次贝叶斯^[104]	力敏电阻	PhysioNet	足部压力信号	帕金森、亨廷顿氏病、肌肉萎缩性硬化	86.96
KNN^[105]	Kinect V2	**	人体25个关节节点的位置和速度	偏瘫，帕金森	79.03
SVM^[105]	6台红外相机组成的运动捕捉系统	**	肩部、肘部、臀部、膝盖和脚踝的角度、时空参数	偏瘫、帕金森、腰部疼痛、背部疼痛	97.90
DT^[105]	6台红外相机组成的运动捕捉系统	**	肩部、肘部、臀部、膝盖和脚踝的角度、时空参数	偏瘫、帕金森、腰部疼痛、背部疼痛	90.10
KNN^[105]	6台红外相机组成的运动捕捉系统	**	肩部、肘部、臀部、膝盖和脚踝的角度、时空参数	偏瘫、帕金森、腰部疼痛、背部疼痛	100.00
RF^[105]	6台红外相机组成的运动捕捉系统	**	肩部、肘部、臀部、膝盖和脚踝的角度、时空参数	偏瘫、帕金森、腰部疼痛、背部疼痛	99.30

[1]	Qian XU,Ying LI,Gang WANG. Pedestrian-vehicle detection based on deep learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1661-1667.
[2]	Wan-fu GAO,Ping ZHANG,Liang HU. Nonlinear feature selection method based on dynamic change of selected features [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1293-1300.
[3]	Dan⁃tong OUYANG,Jun XIAO,Yu⁃xin YE. Distant supervision for relation extraction with weakconstraints of entity pairs [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 912-919.
[4]	GU Hai-jun, TIAN Ya-qian, CUI Ying. Intelligent interactive agent for home service [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1578-1585.
[5]	DONG Sa, LIU Da-you, OUYANG Ruo-chuan, ZHU Yun-gang, LI Li-na. Logistic regression classification in networked data with heterophily based on second-order Markov assumption [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1571-1577.
[6]	WANG Xu, OUYANG Ji-hong, CHEN Gui-fen. Measurement of graph similarity based on vertical dimension sequence dynamic time warping method [J]. 吉林大学学报(工学版), 2018, 48(4): 1199-1205.
[7]	ZHANG Hao, ZHAN Meng-ping, GUO Liu-xiang, LI Zhi, LIU Yuan-ning, ZHANG Chun-he, CHANG Hao-wu, WANG Zhi-qiang. Human exogenous plant miRNA cross-kingdom regulatory modeling based on high-throughout data [J]. 吉林大学学报(工学版), 2018, 48(4): 1206-1213.
[8]	LI Xiong-fei, FENG Ting-ting, LUO Shi, ZHANG Xiao-li. Automatic music composition algorithm based on recurrent neural network [J]. 吉林大学学报(工学版), 2018, 48(3): 866-873.
[9]	LIU Jie, ZHANG Ping, GAO Wan-fu. Feature selection method based on conditional relevance [J]. 吉林大学学报(工学版), 2018, 48(3): 874-881.
[10]	HUANG Lan, JI Lin-ying, YAO Gang, ZHAI Rui-feng, BAI Tian. Construction of disease-symptom semantic net for misdiagnosis prompt [J]. 吉林大学学报(工学版), 2018, 48(3): 859-865.
[11]	WANG Xu, OUYANG Ji-hong, CHEN Gui-fen. Heuristic algorithm of all common subsequences of multiple sequences for measuring multiple graphs similarity [J]. 吉林大学学报(工学版), 2018, 48(2): 526-532.
[12]	LIU Xue-juan, YUAN Jia-bin, XU Juan, DUAN Bo-jia. Quantum k-means algorithm [J]. 吉林大学学报(工学版), 2018, 48(2): 539-544.
[13]	YANG Xin, XIA Si-jun, LIU Dong-xue, FEI Shu-min, HU Yin-ji. Target tracking based on improved accelerated gradient under tracking-learning-detection framework [J]. 吉林大学学报(工学版), 2018, 48(2): 533-538.
[14]	LI Jia-fei, SUN Xiao-yu. Clustering method for uncertain data based on spectral decomposition [J]. 吉林大学学报(工学版), 2017, 47(5): 1604-1611.
[15]	QU Hui-yan, ZHAO Wei, QIN Ai-hong. A fast collision detection algorithm based on optimization operator [J]. 吉林大学学报(工学版), 2017, 47(5): 1598-1603.

Methods and technologies of human gait recognition

RICH HTML

PDF (PC)

Abstract

Cite this article

share this article

Figures/Tables 21

References 105

Related Articles 15

Metrics

Comments

Recommended 0