人体步态识别方法与技术

doi:10.13229/j.cnki.jdxbgxb20190264

摘要/Abstract

摘要：

针对人体步态识别，从步态数据采集仪器、常见步态数据集、步态参数提取和步态识别方法4个方面分别展开综述。首先，介绍常用的步态数据采集仪器的优缺点、可靠性和应用场景；其次，从建立机构、样本容量、采样率、环境、仪器和变量6个方面对常用的步态数据集进行对比分析；然后，将现有步态参数提取方法分为基于模型的方法和基于非模型的方法进行详细阐述，进而在步态识别算法方面分别从支持向量机、自编码器和卷积神经网络三方面进行介绍，并对上述方法从身份识别和异常步态辨识两个应用方向分别展开对比；最后，结合实际应用指出当前研究存在的不足和未来的发展方向。

关键词: 人工智能, 步态数据采集, 步态数据集, 步态参数提取, 步态识别

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

中图分类号:

TP18

李贻斌,郭佳旻,张勤. 人体步态识别方法与技术[J]. 吉林大学学报(工学版), 2020, 50(1): 1-18.

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.

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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

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