Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (12): 2954-2963.doi: 10.13229/j.cnki.jdxbgxb20210481

Previous Articles     Next Articles

Cross⁃modality person re⁃identification based on semantic coupling and identity⁃consistence constraint

Chun-ping HOU(),Qing-yuan YANG,Mei-yan HUANG,Zhi-peng WANG   

  1. School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China
  • Received:2021-05-31 Online:2022-12-01 Published:2022-12-08

Abstract:

To solve the problem of the large inter-modality discrepancy and the intra-class variations in cross modality person re-Identification (CM-Reid), a novel CM-Reid framework based on semantic coupling and identity-consistence constraint was proposed. In the semantic level, the semantic representations bi-directionally and fuse the semantic information between different modalities were coupled to alleviate the inter-modality discrepancy. In the identity level, the cross-modality triplet loss and identity loss to maintain the identity consistence were optimized to alleviate the intra-class variations. The experimental results show that the proposed method can effectively improve the performance of CM-Reid. Compared with the baseline method, the accuracy of Top-1 and mAP indicators is improved by more than 10%.

Key words: computer application, cross-modality person re-identification, deep learning, semantic coupling, identity-consistence constrain

CLC Number: 

  • TP391

Fig.1

Framework of the proposed method"

Fig.2

Semantic coupling module"

Table 1

Effectiveness comparison of SCM"

序号方法All SearchIndoor Search
Top-1mAPTop-1mAP
1Baseline45.8144.8547.0646.13
2Baseline+SCM_block146.0145.1547.9846.05
3Baseline+SCM_block248.9748.0050.1049.25
4Baseline+SCM_block354.1052.6155.8352.96
5Baseline+SCM_block457.8555.3358.1556.19

Table 2

Cross-modality triplet loss validity verification"

方法All SearchIndoor Search
Top-1mAPTop-1mAP
155.9254.2056.8554.92
257.8555.3358.1556.19

Table 3

Comparision experiment on SYSY-MM01 dataset"

方法All SearchIndoor Search
Top-1Top-10Top-20mAPTop-1Top-10Top-20mAP
One-stream712.0449.6866.7413.6716.9463.5582.1022.95
Two-stream711.6547.9965.5012.8515.6061.1881.0221.49
Zero-Padding714.8054.1271.3315.9520.5868.3885.7926.92
SDL1628.1270.2383.6729.0132.5680.4590.6739.56
MSR1737.3583.4093.3438.1139.6489.2997.6650.88
MACE1851.6487.2594.4450.1157.3593.0297.4764.79
HSM920.6832.7477.9523.21----
BDTR1927.3266.9681.0727.3231.9277.1889.2841.86
eBDTR1927.8267.3481.3428.4232.4677.4289.6242.46
expAT2038.5776.6486.3938.6144.7169.8277.8732.20
CMSP2143.5686.25-44.9848.6289.50-57.50
cmGAN2226.9767.5180.5631.4931.6377.2389.1842.19
D2RL1128.9070.6082.4029.20----
JSIA2338.1080.7089.9036.9043.8086.2094.2052.90
AliGAN1042.4085.0093.7040.7045.9087.6094.4054.30
本文57.8589.2594.4555.3358.1590.0295.6356.19

Table 4

Comparision experiment on RegDB dataset"

方法RGB→ThermalThermal→RGB
Top-1Top-10Top-20mAPTop-1Top-10Top-20mAP
Zero-Padding717.7534.2144.3518.9016.6334.6844.2517.82
SDL1626.4751.3461.2223.5825.7450.2359.6622.89
MSR1748.4370.3279.9548.67----
MACE1872.3788.4093.5969.0972.1288.0793.0768.57
BDTR1933.5658.6167.4332.7632.9258.4668.4331.96
eBDTR1934.6258.9668.7233.4634.2158.7468.6432.49
HSME950.8573.3681.6647.0050.1572.4081.0746.16
expAT2067.45--66.5166.48--67.31
CMSP2165.0783.71-64.50----
D2RL1143.4066.1076.3044.10----
JSIA2348.50--48.90----
AliGAN1057.90--53.6056.30--53.40
本文75.1290.5696.0974.5174.9691.0295.7676.10

Fig.3

Retrieval result of RGB image to thermal images"

Fig.4

Retrieval result of thermal image to RGB images"

1 Ye Mang, Shen Jian-bing, Lin Gao-jie, et al. Deep learning for person re-identification: a survey and outlook[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(6): 2872-2893.
2 Zhong Zhun, Zheng Liang, Luo Zhi-ming, et al. Learning to adapt invariance in memory for person re-identification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(8): 2723-2738.
3 Qian Xue-lin, Fu Yan-wei, Tao Xiang, et al. Leader-based multi-scale attention deep architecture for person re-identification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 42(2): 371-385.
4 马淼, 王云涛, 潘海鹏. 基于注意力机制和空间几何约束的行人重识别方法[J]. 吉林大学学报: 工学版, 2022, 52(5): 1079-1087.
Ma Miao, Wang Yun-tao, Pan Hai-peng. Person re-identification method based on attention mechanism and spatial geometric constraint[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(5): 1079-1087.
5 Chen Guang-yi, Lu Ji-wen, Yang Ming, et al. Learning recurrent 3D attention for video-based person re-identification[J]. IEEE Transactions on Image Processing, 2020, 29: 6963-6976.
6 Wang K, Ding C, Maybank S J, et al. CDPM: convolutional deformable part models for semantically aligned person re-identification[J]. IEEE Transactions on Image Processing, 2019, 29: 3416-3428.
7 Wu A, Zheng W S, Yu H X, et al. RGB-infrared cross-modality person re-identification[C]∥Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 2017: 5380-5389.
8 Ye Mang, Wang Zheng, Lan Xiang-yuan, et al. Visible thermal person re-identification via dual-constrained top-ranking[C]∥International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 2018:1092-1099.
9 Hao Yi, Wang Nan-nan, Li Jie, et al. HSME: hypersphere manifold embedding for visible thermal person re-identification[C]∥Proceedings of the AAAI Conference on Artificial Intelligence, Hawaii, USA,2019: 8385-8392.
10 Wang Guan-an, Zhang Tian-zhu, Cheng Jian, et al. RGB-infrared cross-modality person re-identification via joint pixel and feature alignment[C]∥Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 2019: 3623-3632.
11 Wang Zhi-xiang, Wang Zheng, Zheng Yin-qiang, et al. Learning to reduce dual-level discrepancy for infrared-visible person re-identification[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 618-626.
12 Deng J, Dong W, Socher R, et al. ImageNet: a large-scale hierarchical image database[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, 2009: 248-255.
13 He Kai-ming, Zhang Xiang-yu, Ren Shao-qing, et al. Deep residual learning for image recognition[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770-778.
14 Hermans A, Beyer L, Leibe B. In defense of the triplet loss for person re-identification[DB/OL]. [2020-12-03].
15 Nguyen D T, Hong H G, Kim K W, et al. Person recognition system based on a combination of body images from visible light and thermal cameras[J]. Sensors, 2017, 17(3): No.605.
16 Kansal K, Subramanyam A V, Wang Z, et al. SDL: spectrum-disentangled representation learning for visible-infrared person re-identification[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(10): 3422-3432.
17 Feng Zhan-xiang, Lai Jian-hua, Xie Xiao-hua. Learning modality-specific representations for visible-infrared person re-identification[J]. IEEE Transactions on Image Processing, 2019, 29: 579-590.
18 Ye Mang, Lan Xiang-yuan, Leng Qing-ming, et al. Cross-modality person re-identification via modality-aware collaborative ensemble learning[J]. IEEE Transactions on Image Processing, 2020, 29: 9387-9399.
19 Ye Mang, Lan Xiang-hua, Wang Zheng, et al. Bi-directional center-constrained top-ranking for visible thermal person re-identification[J]. IEEE Transactions on Information Forensics and Security, 2019, 15: 407-419.
20 Ye Han-rong, Liu Hong, Meng Fan-yang, et al. Bi-directional exponential angular triplet loss for RGB-infrared person re-identification[J]. IEEE Transactions on Image Processing, 2021, 30: 1583-1595.
21 Wu An-cong, Zheng Wei-shi, Gong Shao-gang, et al. RGB-IR person re-identification by cross-modality similarity preservation[J]. International Journal of Computer Vision, 2020, 128(6): 1765-1785.
22 Dai P Y, Ji R R, Wang H B, et al. Cross-modality person re-identification with generative adversarial training[C]∥Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden: 2018: 677-683.
23 Wang G A, Zhang T, Yang Y, et al. Cross-modality paired-images generation for RGB-infrared person re-identification[C]∥Proceedings of the AAAI Conference on Artificial Intelligence, New York, USA, 2020: 12144-12151.
24 胡静,陶洋.基于RPCA的群稀疏表示人脸识别方法[J]. 重庆邮电大学学报: 自然科学版, 2020, 32(3): 459-468.
Hu Jing, Tao Yang. Group sparse representation face recognition method based on RPCA[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2020, 32(3): 459-468.
25 李新春,马红艳,林森.基于局部邻域四值模式的掌纹掌脉融合识别[J].重庆邮电大学学报: 自然科学版, 2020, 32(4): 630-638.
Li Xin-chun, Ma Hong-yan, Lin Sen. Palmprint and palm vein fusion recognition based on local neighbor quaternary pattern[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2021, 47(5): 285-291, 300.
[1] Xian-yu QI,Wei WANG,Lin WANG,Yu-fei ZHAO,Yan-peng DONG. Semantic topological map building with object semantic grid map [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 569-575.
[2] Xiao-hu SHI,Jia-qi WU,Chun-guo WU,Shi CHENG,Xiao-hui WENG,Zhi-yong CHANG. Residual network based curve enhanced lane detection method [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 584-592.
[3] Peng GUO,Wen-chao ZHAO,Kun LEI. Dual⁃resource constrained flexible job shop optimal scheduling based on an improved Jaya algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 480-487.
[4] Jin-Zhen Liu,Guo-Hui Gao,Hui Xiong. Multi⁃scale attention network for brain tissue segmentation [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 576-583.
[5] Jin-wu GAO,Zhi-huan JIA,Xiang-yang WANG,Hao XING. Degradation trend prediction of proton exchange membrane fuel cell based on PSO⁃LSTM [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 2192-2202.
[6] Feng-feng ZHOU,Hai-yang ZHU. SEE: sense EEG⁃based emotion algorithm via three⁃step feature selection strategy [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1834-1841.
[7] Xuan-jing SHEN,Xue-feng ZHANG,Yu WANG,Yu-bo JIN. Multi⁃focus image fusion algorithm based on pixel⁃level convolutional neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1857-1864.
[8] Fu-heng QU,Tian-yu DING,Yang LU,Yong YANG,Ya-ting HU. Fast image codeword search algorithm based on neighborhood similarity [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1865-1871.
[9] Tian BAI,Ming-wei XU,Si-ming LIU,Ji-an ZHANG,Zhe WANG. Dispute focus identification of pleading text based on deep neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1872-1880.
[10] Xiao-ying LI,Ming YANG,Rui QUAN,Bao-hua TAN. Unbalanced text classification method based on deep learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1889-1895.
[11] Gui-he QIN,Jun-feng HUANG,Ming-hui SUN. Text input based on two⁃handed keyboard in virtual environment [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1881-1888.
[12] Dan HU,Xin MENG. Vessel search method by earth observation satellite based on time⁃varying grid [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1896-1903.
[13] Jun WANG,Yan-hui XU,Li LI. Data fusion privacy protection method with low energy consumption and integrity verification [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1657-1665.
[14] Feng-feng ZHOU,Yi-chi ZHANG. Unsupervised feature engineering algorithm BioSAE based on sparse autoencoder [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1645-1656.
[15] Ming-hua GAO,Can YANG. Traffic target detection method based on improved convolution neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1353-1361.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] AN Shi, LI Jing, CUI Na . Simulation of commuter day-to-day dynamic route switching behavior under ATIS information[J]. 吉林大学学报(工学版), 2009, 39(03): 587 -0592 .
[2] DUAN Bin, SUN Tong-jing,LI Zhen-hua, HUANG Chang-wei,ZHANG Guang-xian. IIR Butterworth digital filter for full digital inverter[J]. 吉林大学学报(工学版), 2009, 39(增刊2): 311 -0314 .
[3] JIN Jing-Fu, MA Yi, LIU Yu-Rong, CONG Qian. Aerodynamic analysis of the family of airfoil of owl[J]. 吉林大学学报(工学版), 2010, 40(增刊): 278 -0281 .
[4] Jin Lisheng, Wang Rongben, Gao Long, Guo Lie. Shadow path mark segmentation method based on regiongrowing for intelligent vehicle[J]. 吉林大学学报(工学版), 2006, 36(增刊1): 132 -0135 .
[5] WANG Qiang,DAI Jing-min,HE Xiao-wa. Effect of time delay on thermal conductivity measurement with transient planar heat source technique[J]. 吉林大学学报(工学版), 2011, 41(03): 711 -715 .
[6] MA Kai, GUAN Hsin, PANG Shu-yi, ZHAN Jun. Interval control method on consistency of suspension kinematics[J]. 吉林大学学报(工学版), 2011, 41(4): 910 -914 .
[7] . [J]. 吉林大学学报(工学版), 2007, 37(06): 0 .
[8] Wei Hai-bin, Liu Han-bing, Gao Yi-ping, Li Chang-yu, Fang Ying . Effect of freezethaw cycles on dynamic strength of fly ash soils
[J]. 吉林大学学报(工学版), 2007, 37(02): 329 -0333 .
[9] Meng Guang-wei,Li Feng, Zhao Yun-liang. Reliability analysis of fatigue and fracture based on stochastic finite element method[J]. 吉林大学学报(工学版), 2006, 36(增刊1): 16 -0019 .
[10] GUO Zi-Zheng, CHEN Chong-Shuang, WANG Xin, CHEN Ya-Qing, TAN Yong-Gang. Driving behavior risk status identification |model based on FCM[J]. 吉林大学学报(工学版), 2010, 40(02): 427 -0430 .