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Journals >
Laser & Optoelectronics Progress >
Volume 57 >
Issue 6 >
Page 061001 > Article
Laser & Optoelectronics Progress
Vol. 57, Issue 6, 061001 (2020)
Pedestrian Attribute Recognition Based on Deep Learning
Peipei Yuan and Liang Zhang
*
Author Affiliations
Tianjin Key Laboratory of Advanced Signal and Image Processing, Civil Aviation University of China, Tianjin 300300, China
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DOI:
10.3788/LOP57.061001
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Peipei Yuan, Liang Zhang. Pedestrian Attribute Recognition Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061001
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Fig. 1.
Pedestrian attribute recognition network
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Fig. 2.
Structure of residual block
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Fig. 3.
Examples of image segmentation. (a) Image to be segmented; (b) result of semantic segmentation; (c) result of instance segmentation
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Fig. 4.
Samples of pedestrian images. (a)-(f) Sample 1-6
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Fig. 5.
Variation trend of regional contrast loss function during training
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Fig. 6.
Result of attribute recognition
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Attribute
Fig. 4
(a)-(c)
Fig. 4
(d)-(f)
Ratio/%
Age 16-30
0
0
49.7
Age 31-45
0
1
32.9
Age 46-60
0
0
10.2
Age above 61
1
0
6.2
Backpack
0
0
19.7
Carrying other
0
0
19.9
Casual lower
1
1
86.1
Casual upper
1
1
85.3
Formal lower
0
0
13.8
Formal upper
0
0
13.4
Hat
1
1
10.2
Jacket
0
0
6.9
Jeans
0
0
30.6
Leather shoes
1
0
29.6
Logo
0
0
4.0
Long hair
0
0
23.8
Male
1
0
54.9
Messenger bag
0
1
29.6
Muffler
0
1
8.4
No accessory
0
0
74.9
No carrying
1
0
27.6
Plaid
0
0
2.7
Plastic bag
0
0
7.7
Sandals
0
0
0.2
Shoes
0
1
36.3
Shorts
0
0
3.5
Short sleeve
0
0
14.2
Skirt
0
0
4.6
Sneaker
0
0
21.6
Stripes
0
0
1.7
Sunglasses
0
0
2.9
Trousers
1
1
51.5
T-shirt
0
0
8.4
Upper other
1
1
45.6
V-neck
0
0
1.2
Table 1.
Label of pedestrian image and the ratio of attribute
Algorithm
E
mA
E
acc
E
prec
E
rec
F
1
ACN
[
21
]
81.15
73.66
84.06
81.26
82.64
DeepMAR
[
5
]
82.89
75.07
83.68
83.14
83.41
FSPP
[
20
]
81.67
75.72
84.84
83.10
83.96
HP-Net
[
7
]
81.77
76.13
84.92
83.24
84.07
PGDM
[
18
]
82.97
78.08
86.86
84.68
85.76
Experiment1
81.55
76.03
85.20
82.85
84.10
Experiment2
83.37
78.56
87.63
85.03
86.24
Experiment3
84.49
79.44
87.82
85.94
86.87
Table 2.
Experimental results of PETA dataset%
Algorithm
E
mA
E
acc
E
prec
E
rec
F
1
ACN
[
21
]
69.66
62.61
80.12
72.26
75.98
DeepMAR
[
5
]
73.79
62.02
74.92
76.21
75.56
FSPP
[
20
]
79.64
60.25
69.10
80.16
74.21
HP-Net
[
7
]
76.12
65.39
77.33
78.79
78.05
PGDM
[
18
]
74.31
64.57
78.86
75.90
77.35
Experiment1
79.01
66.85
79.47
80.04
77.94
Experiment2
79.70
67.02
80.54
80.47
78.38
Experiment3
80.11
67.68
81.60
81.55
79.62
Table 3.
Experimental results of RAP dataset%
Abstract
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Peipei Yuan, Liang Zhang. Pedestrian Attribute Recognition Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061001
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Paper Information
Category: Image Processing
Received: Jun. 26, 2019
Accepted: Aug. 21, 2019
Published Online: Mar. 5, 2020
The Author Email: Zhang Liang (l-zhang@cauc.edu.cn)
DOI:
10.3788/LOP57.061001
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