Pedestrian Attribute Recognition Algorithm Based on Multi-Scale Attention Network

Table 1. Verification experiment of the channel attention effectiveness unit: %
View table
Table 1. Verification experiment of the channel attention effectiveness unit: %
Methods RAP PA-100K
mA F1 mA F1
Baseline 75.67 78.20 77.28 84.52
Baseline+CA 75.89 78.36 77.35 84.67

Table 2. Recognition results of different feature fusion modules unit: %
View table
Table 2. Recognition results of different feature fusion modules unit: %
Method RAP PA-100K
mA F1 mA F1
Baseline 75.67 78.20 77.28 84.52
Top-down(addition) 74.23 78.01 76.65 84.63
Top-down(weight) 76.75 78.96 78.13 84.95
Top-down(weight)+CA 78.70 80.12 79.82 85.71

Table 3. Recognition results for each layer feature on RAP data set unit: %

View table

Table 3. Recognition results for each layer feature on RAP data set unit: %

Feature layer	Age less 16	Age 17-60	Age bigger 60	ub-shirt	lb-skirt	ub-short sleeve	mA
conv2	49.54	49.36	50.12	51.23	50.56	54.57	61.29
conv3	50.12	49.78	48.93	50.54	54.18	69.55	65.46
conv4	49.82	49.70	47.65	49.73	53.90	79.78	62.91
p5'	56.76	52.59	60.99	71.93	75.68	77.21	77.01
p4'	61.24	54.42	64.23	78.24	74.76	79.92	77.23
p3'	62.35	56.74	67.42	78.89	77.15	80.54	76.89
p2'	62.27	56.67	66.37	77.97	75.43	80.35	77.65
Baseline	63.36	58.49	71.17	78.52	78.82	79.15	75.67
Ours	76.42	75.56	66.92	79.14	80.14	78.66	78.70

Table 4. Recognition results of different algorithms on the RAP data set unit: %

View table

Table 4. Recognition results of different algorithms on the RAP data set unit: %

Algorithm	mA	Acc	Prec	Rec	F1
ACN	69.66	62.61	80.12	72.26	75.98
DeepMar	73.79	62.02	74.92	76.21	75.56
HP-Net	76.12	65.39	77.33	78.79	78.05
VeSPA	77.70	67.35	79.51	79.67	79.59
PGDM	74.31	64.57	78.86	75.90	77.35
IA2-Net	77.44	65.75	79.01	77.45	78.03
Ours	78.70	68.17	78.89	79.98	80.12

Table 5. Recognition results of different algorithms on the PA-100K data set unit: %

View table

Table 5. Recognition results of different algorithms on the PA-100K data set unit: %

Algorithm	mA	Acc	Prec	Rec	F1
DeepMar	72.70	70.39	82.24	80.42	81.32
HP-Net	74.21	72.19	82.97	82.09	82.53
VeSPA	76.32	73.00	84.99	81.49	83.20
PGDM	74.95	73.08	84.36	82.24	83.29
IA2-Net	77.28	74.73	83.34	85.73	84.52
Ours	79.82	78.17	82.83	84.98	85.71

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Na Li, Yangyang Wu, Ying Liu, Jin Xing. Pedestrian Attribute Recognition Algorithm Based on Multi-Scale Attention Network[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410025

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Paper Information

Category: Image Processing

Received: Sep. 27, 2020

Accepted: Nov. 5, 2020

Published Online: Feb. 22, 2021

The Author Email: Li Na (lina114@xupt.edu.cn), Wu Yangyang (lina114@xupt.edu.cn)

DOI:10.3788/LOP202158.0410025

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Verification experiment of the channel attention effectiveness unit: %

Table 1. Verification experiment of the channel attention effectiveness unit: %

Table 2. Recognition results of different feature fusion modules unit: %

Table 2. Recognition results of different feature fusion modules unit: %

Table 3. Recognition results for each layer feature on RAP data set unit: %

Table 3. Recognition results for each layer feature on RAP data set unit: %

Table 4. Recognition results of different algorithms on the RAP data set unit: %

Table 4. Recognition results of different algorithms on the RAP data set unit: %

Table 5. Recognition results of different algorithms on the PA-100K data set unit: %

Table 5. Recognition results of different algorithms on the PA-100K data set unit: %