Person Re-Identification Based on First-Order and Second-Order Spatial Information

Sha Liu; Jianwu Dang; Song Wang; Yangping Wang

doi:10.3788/LOP202158.0215005

Laser & Optoelectronics Progress, Volume. 58, Issue 2, 0215005(2021)

Person Re-Identification Based on First-Order and Second-Order Spatial Information

Sha Liu¹, Jianwu Dang^1,2、*, Song Wang^1,2, and Yangping Wang^2,3

Author Affiliations

¹School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China

²Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphic & Image Processing, Lanzhou, Gansu 730070, China;

³National Experimental Teaching Demonstration Center on Computer Science and Technology, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China

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Figures & Tables(11)

Fig. 1. Architecture of person re-identification network based on first-order and second-order spatial information

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Fig. 2. Structure of the first-order spatial mask

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Fig. 3. Schematic diagram of the second-order spatial mask

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Fig. 4. Structure of the second-order spatial mask

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Fig. 5. Two-dimensional visualization of sample distribution supervised by different losses. (a) Label smoothed cross entropy loss and basic triplet loss; (b) label smoothed cross entropy loss and center triplet loss

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Fig. 6. Sample heat map. (a) Sample heat map obtained using only SOM; (b) sample heat map obtained using OM and SOM

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Fig. 7. Visualization results of Market-1501 data set. (a) Baseline network algorithm; (b) our algorithm

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Fig. 8. Visualization results of DukeMTMC-reID data set. (a) Baseline network algorithm; (b) our algorithm

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Table 1. Experimental results of different models unit: %

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Table 1. Experimental results of different models unit: %

NO.	Model	Loss	Market-1501		DukeMTMC-reID
NO.	Model	Loss	Rank-1	mAP	Rank-1	mAP
1	Baseline	S+BT	95.36	87.62	88.28	76.93
2	Baseline+ SOM	S+BT	95.53	88.63	89.55	78.18
3	Baseline+SOM+ OM	S+BT	95.94	88.90	90.08	78.49
4	Baseline+ SOM+ OM	S+CT	96.19	89.71	90.37	79.23

Table 2. Recognition results of different algorithms on the Market-1501 data set unit: %
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Table 2. Recognition results of different algorithms on the Market-1501 data set unit: %
Algorithm Rank-1 mAP
GAN^[26] 78.12 56.25
SVDNet^[27] 82.30 62.10
AACN^[28] 85.90 66.87
HA-CNN^[14] 89.00 71.25
GLAD^[29] 89.90 73.90
PCB+RPP^[11] 93.80 81.60
MGN^[30] 95.70 86.90
Pyramid-Net^[31] 95.70 88.20
Our algorithm 96.19 89.71

Table 3. Recognition results of different algorithms on the DukeMTMC-reID data set unit: %
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Table 3. Recognition results of different algorithms on the DukeMTMC-reID data set unit: %
Algorithm Rank-1 mAP
GAN^[26] 67.68 47.13
SVDNet^[27] 76.70 56.80
AACN^[28] 76.84 58.25
HA-CNN^[14] 78.50 60.25
GLAD^[29] 80.00 62.20
PCB+RPP^[11] 83.30 69.20
MGN^[30] 88.70 78.40
Pyramid-Net^[31] 89.00 79.00
Our algorithm 90.37 79.23

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Sha Liu, Jianwu Dang, Song Wang, Yangping Wang. Person Re-Identification Based on First-Order and Second-Order Spatial Information[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0215005

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Paper Information

Category: Machine Vision

Received: Jun. 5, 2020

Accepted: Jul. 20, 2020

Published Online: Jan. 11, 2021

The Author Email: Dang Jianwu (dangjw@mail.lzjt.cn)

DOI:10.3788/LOP202158.0215005

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Experimental results of different models unit: %

Table 1. Experimental results of different models unit: %

Table 2. Recognition results of different algorithms on the Market-1501 data set unit: %

Table 2. Recognition results of different algorithms on the Market-1501 data set unit: %

Table 3. Recognition results of different algorithms on the DukeMTMC-reID data set unit: %

Table 3. Recognition results of different algorithms on the DukeMTMC-reID data set unit: %