一种时空卷积的步态识别方法

doi:10.19665/j.issn1001-2400.2021.04.019

Abstract

Abstract:

As a unique biological characteristic that can be recognized from a distance,gait has a wide application prospect in the fields of identity identification,public security and medical diagnosis.However,the accuracy of gait recognition will be affected by external factors,such as the shooting angle,the pedestrian’s wearing and the change in the status of carrying a bag.Based on the above problems,this paper puts forward the gait recognition method based on spatial-temporal convolution,which uses the convolution neural network to extract gait features,and employs the repeated extraction of the gait features of adjacent frames to make up for the missing information,so that more spatial-temporal information can be obtained.Finally,the proposed method is validated on the CASIA-B dataset.Experimental results show that the proposed method can improve the rank-1 accuracy when a pedestrian walks normally,carrying a bag and wearing a coat.

Key words: gait recognition, spatial-temporal convolution, gait feature, spatial-temporal information

CLC Number:

TP391.41

XU Huanhuan,LI Hongmei,LI Fuyu,SUN Xuemei. Gait recognition method based on spatial-temporal convolution[J].Journal of Xidian University, 2021, 48(4): 144-150.

Figures/Tables 7

References 19

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Gallery NM#1-4		0°~180°											平均值
Probe		0°	18°	36°	54°	72°	90°	108°	126°	144°	162°	180°	平均值
正常	ViDP^[16]				59.1		50.2		57.5
	CMCC^[17]	46.3			52.4		48.3		56.9
	CNN-LB^[18]	54.8			77.8		64.9		76.1
	GaitSet^[2]	64.6	83.3	90.4	86.5	80.2	75.5	80.3	86	87.1	81.4	59.6	79.5
	文中	69.8	82.2	88.5	86.7	79.5	74.0	79.6	86.0	87.6	81.5	65.2	80.0
携带包	GaitSet^[2]	55.8	70.5	76.9	75.5	69.7	63.4	68	75.8	76.2	70.7	52.5	68.6
	文中	59.6	72.0	78.2	75.8	69.8	64.6	68.2	77.0	76.5	70.8	56.3	69.9
穿外套	GaitSet^[2]	29.4	43.1	49.5	48.7	42.3	40.3	44.9	47.4	43	35.7	25.6	40.9
	文中	31.8	46.5	51.0	48.2	44.0	41.7	43.2	45.3	40.9	35.2	27.5	41.4

Gallery NM#1-4	0°~180°				平均值	36°~144°			平均值
Probe NM #5-6	0°	54°	90°	126°	平均值	54°	90°	126°	平均值
CMCC^[17]	46.3	52.4	48.3	56.9	51.0
ViDP^[16]	59.1	50.2	57.5		83.5	76.7	80.7	80.3
MT^[18]	54.8	77.8	64.9	76.1	68.4	90.8	85.8	90.4	89.0
文中	69.8	86.7	74.0	86.0	79.1	96.5	93.3	96.1	95.3

Gallery NM#1-4	0°~180°				平均值	36°~144°			平均值
Probe NM #5-6	0°	54°	90°	126°	平均值	54°	90°	126°	平均值
ViDP^[16]		64.2	60.4	65.0		87	87.7	89.3	88.0
LB^[18]	82.6	94.3	87.4	94.0	89.6	98.0	98.0	99.2	98.4
GaitNet^[3]	91.2	95.6	92.6	96.0	93.9	99.1	99.0	99.2	99.1
文中	91.3	96.7	90.3	97.5	94.0	99.7	97.2	99.8	98.9
Probe BG #1-2	0°	54°	90°	126°	平均值	54°	90°	126°	平均值
LB-subGEI^[18]	64.2	76.9	63.1	76.9	70.3	89.2	84.3	91.0	88.2
GaitNet^[3]	83.0	86.6	74.8	85.8	82.6	90.0	85.6	92.7	89.4
文中	81.8	89.7	78.7	90.9	85.3	95.0	92.0	96.8	94.6
Probe CL #1-2	0°	54°	90°	126°	平均值	54°	90°	126°	平均值
LB-subGEI^[18]	37.7	61.1	54.6	59.1	53.1	77.3	74.5	74.5	75.4
GaitNet^[3]	42.1	70.7	70.6	69.4	63.2	80.0	81.2	79.4	80.2
文中	60.8	74.1	64.3	71.3	67.6	82.3	79.0	78.5	79.9

Gait recognition method based on spatial-temporal convolution

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