Skip to main content
SpringerLink
Log in

Person Re-identification in Video Surveillance Systems Using Deep Learning: Analysis of the Existing Methods

  • INTELLECTUAL CONTROL SYSTEMS, DATA ANALYSIS
  • Published:
Automation and Remote Control Aims and scope Submit manuscript

Abstract

This paper is devoted to a multifaceted analysis of person re-identification (ReID) in video surveillance systems and modern solution methods using deep learning. The general principles and application of convolutional neural networks for this problem are considered. A classification of person ReID systems is proposed. The existing datasets for training deep neural architectures are studied and approaches to increasing the number of images in databases are described. Approaches to forming human image features are considered. The backbone models of convolutional neural network architectures used for person ReID are analyzed and their modifications as well as training methods are presented. The effectiveness of person ReID is examined on different datasets. Finally, the effectiveness of the existing approaches is estimated in different metrics and the corresponding results are given.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.

REFERENCES

  1. Ye, S., Bohush, R.P., and Chen, H., Person Tracking and Re-identification for Multicamera Indoor Video Surveillance Systems, Pattern Recognit. Image Anal., 2020, no. 30, pp. 827–837. https://doi.org/10.1134/S1054661820040136

  2. Ye, M., Shen, J., Lin, G., Xiang, T., Shao, L., and Hoi, S.C., Deep Learning for Person Re-identification: A Survey and Outlook, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. https://doi.org/10.1109/TPAMI.2021.3054775

  3. Zang, X., Li, G., and Gao, W., Multi-direction and Multi-scale Pyramid in Transformer for Video-based Pedestrian Retrieval, arXiv: abs/2202.06014, 2022. https://doi.org/10.1109/TII.2022.3151766

  4. Mihaescu, R., Chindea, M., Paleologu, C., Carata, S., and Ghenescu, M., Person Re-Identification across Data Distributions Based on General Purpose DNN Object Detector, Algorithms, 2020, no. 13, art. no. 343. https://doi.org/10.3390/a13120343

  5. Liu, H., Qin, L., Cheng, Z., and Huang, Q., Set-based Classification for Person Re-identification Utilizing Mutual-information, 2013 IEEE International Conference on Image Processing, 2013, pp. 3078–3082. https://doi.org/10.1109/ICIP.2013.6738634

  6. Gu, X., Chang, H., Ma, B., Bai, S., Shan, S., and Chen, X., Clothes-Changing Person Re-identification with RGB Modality, arXiv: abs/2204.06890, 2022. https://doi.org/10.48550/arXiv.2204.06890

  7. Huang, Y., Wu, Q., Zhong, Y., and Zhang, Z., Clothing Status Awareness for Long-Term Person Re-Idenification, 2021 IEEE/CVF International Conference on Computer Vision, 2021, pp. 11895–11904. https://doi.org/10.1109/ICCV48922.2021.01168

  8. Hao, X., Zhao, S., Ye, M., and Shen, J., Cross-Modality Person Re-identification via Modality Confusion and Center Aggregation, 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 16383–16392. https://doi.org/10.1109/ICCV48922.2021.0160

  9. Zhang, T., Xie, L., Wei, L., Zhuang, Z., Zhang, Y., Li, B., and Tian, Q., UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 11501–11510. https://doi.org/10.1109/CVPR46437.2021.01134

  10. Zhao, F., Liao, S., Xie, G., Zhao, J., Zhang, K., and Shao, L., Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for Person Re-identification, ECCV 2020, Lecture Notes in Computer Science, vol. 12356, Cham: Springer, 2020, pp. 526–544. https://doi.org/10.1007/978-3-030-58621-8_31

    Book  Google Scholar 

  11. Luo, C., Song, C., and Zhang, Z., Generalizing Person Re-identification by Camera-Aware Invariance Learning and Cross-Domain Mixup, ECCV 2020, Lecture Notes in Computer Science, vol. 12356, Cham: Springer, 2020, pp. 224–241. https://doi.org/10.1007/978-3-030-58555-6_14

    Book  Google Scholar 

  12. Jin, X., Lan, C., Zeng, W., Chen, Z., and Zhang, L., Style Normalization and Restitution for Generalizable Person Re-identification, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 3140–3149. https://doi.org/10.1109/cvpr42600.2020.00321

  13. Song, J., Yang, Y., Song, Y., Xiang, T., and Hospedales, T.M., Generalizable Person Re-identification by Domain-Invariant Mapping Network, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 719–728. https://doi.org/10.1109/CVPR.2019.00081

  14. Ihnatsyeva, S., Bohush, R., and Ablameyko, S., Joint Dataset for CNN-based Person Re-identification, Proceedings of the 15th International Conference on Pattern Recognition and Information Processing (PRIP’2021), September 21–24, 2021, Minsk: United Institute of Informatics Problems of the National Academy of Sciences of Belarus, 2021, pp. 33–37.

  15. Liao, S., Mo, Z., Hu, Y., and Li, S., Open-set Person Re-identification, arXiv: abs/1408.0872, 2014. https://doi.org/10.48550/arXiv.1408.0872

  16. Li, W., Zhao, R., and Wang, X., Human Reidentification with TransferredMetric Learning, Proceedings of the 11th Asian Conference on Computer Vision (ACCV), 2012. https://doi.org/10.1007/978-3-642-37331-2_3

  17. Li, W. and Wang, X., Locally Aligned Feature Transforms across Views, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 3594–3601. https://doi.org/10.1109/CVPR.2013.461

  18. Li,W., Zhao, R., Xiao, T., and Wang, X., DeepReID: Deep Filter Pairing Neural Network for Person Reidentification, Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 152–159. https://doi.org/10.1109/CVPR.2014.27

  19. Wei, L., Zhang, S., Gao, W., and Tian, Q., Person Transfer GAN to Bridge Domain Gap for Person Re-identification, Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 79–88. https://doi.org/10.1109/CVPR.2018.00016

  20. Ristani, E., Solera, F., Zou, R.S., Cucchiara, R., and Tomasi, C., Performance Measures and a Data Set for Multi-target, Multi-camera Tracking, arXiv: abs/1609.01775, 2016. https://doi.org/10.1007/978-3-319-48881-3_2

  21. Exposing.ai. Duke MTMC. URL: https://exposing.ai/duke mtmc.

  22. Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., and Tian, Q., Person Re-identification in the Wild, Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 3346–3355. https://doi.org/10.1109/CVPR.2017.357

  23. Xiao, T., Li, S., Wang, B., Lin, L., and Wang, X., Joint Detection and Identification Feature Learning for Person Search, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 3376–3385. https://doi.org/10.1109/CVPR.2017.360

  24. Zheng, L., Bie, Z., Sun, Y., Wang, J., Su, C., Wang, S., and Tian, Q., MARS: A Video Benchmark for Large-Scale Person Re-identification, ECCV 2016, Lecture Notes in Computer Science, vol. 9910, Cham: Springer, 2016, pp. 863–884. https://doi.org/10.1007/978-3-319-46466-4_52

    Book  Google Scholar 

  25. Song, G., Leng, B., Liu, Y., Hetang, C., and Cai, S., Region-based Quality Estimation Network for Large-scale Person Re-identification, arXiv: abs/1711.08766, 2018. https://doi.org/10.48550/arXiv.1711.08766

  26. Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q., Scalable Person Re-identification: A Benchmark, Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1116–1124. https://doi.org/10.1109/ICCV.2015.133

  27. Gray, D., Brennan, S., and Tao, H., Evaluating Appearance Models for Recognition, Reacquisition, and Tracking, Proceedings of the IEEE Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2007.

  28. Hirzer, M., Beleznai, C., Roth, P.M., and Bischof, H., Person Re-identification by Descriptive and Discriminative Classification, SCIA, Lecture Notes in Computer Science, vol. 6688, Berlin–Heidelberg: Springer, 2011, pp. 91–102. https://doi.org/10.1007/978-3-642-21227-79

    Book  Google Scholar 

  29. Zheng, W., Gong, S., and Xiang, T., UnrealPerson: An Adaptive Associating Groups of People, BMVC, 2009. https://doi.org/10.5244/C.23.23

    Book  Google Scholar 

  30. Karanam, S., Gou, M., Wu, Z., Rates-Borras, A., Camps, O.I., and Radke, R.J., A Systematic Evaluation and Benchmark for Person Re-identification: Features, Metrics, and Datasets, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, no. 41, pp. 523–536. https://doi.org/10.1109/TPAMI.2018.2807450

  31. Ihnatsyeva, S. and Bohush, R., PolReID, 2021. URL: https://github.com/SvetlanaIgn/PolReID

  32. Li, S., Xiao, T., Li, H., Zhou, B., Yue, D., and Wang, X., Person Search with Natural Language Description, Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 5187–5196. https://doi.org/10.1109/CVPR.2017.551

  33. Ding, Z., Ding, C., Shao, Z., and Tao, D., Semantically Self-Aligned Network for Text-to-Image Partaware Person Re-identification, arXiv: abs/2107.12666, 2021.

  34. Li, X., Zheng, W., Wang, X., Xiang, T., and Gong, S., Multi-Scale Learning for Low-Resolution Person Re-identification, Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), 2015, pp. 3765–3773. https://doi.org/10.1109/ICCV.2015.429

  35. Jing, X., Zhu, X., Wu, F., Hu, R., You, X., Wang, Y., Feng, H., and Yang, J., Super-Resolution Person Re-identification with Semi-Coupled Low-Rank Discriminant Dictionary Learning, IEEE Transactions on Image Processing, 2015, no. 26, pp. 1363–1378. https://doi.org/10.1109/TIP.2017.2651364

  36. Wu, A., Zheng, W., Yu, H., Gong, S., and Lai, J., RGB-Infrared Cross-Modality Person Reidentification, IEEE International Conference on Computer Vision (ICCV), 2017, pp. 5390–5399. https://doi.org/10.1109/ICCV.2017.575

  37. Nguyen, T.D., Hong, H.G., Kim, K., and Park, K.R., Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras, Sensors, 2017, no. 17. https://doi.org/10.3390/s17030605

  38. Pang, L., Wang, Y., Song, Y., Huang, T., and Tian, Y., Cross-Domain Adversarial Feature Learning for Sketch Re-identification, Proceedings of the 26th ACM International Conference on Multimedia, 2018. https://doi.org/10.1145/3240508.3240606

  39. Xiao, T., Li, S., Wang, B., Lin, L., and Wang, X., End-to-end Deep Learning for Person Search, arXiv:abs/1604.01850, 2016.

  40. Layne, R., Hospedales, T.M., and Gong, S., Investigating Open-World Person Re-identification Using a Drone, ECCV Workshops, 2014. https://doi.org/10.1007/978-3-319-16199-016

    Book  Google Scholar 

  41. Fu, D., Chen, D., Bao, J., Yang, H., Yuan, L., Zhang, L., Li, H., and Chen, D., Unsupervised Pretraining for Person Re-identification, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 14745–14754. https://doi.org/10.1109/CVPR46437.2021.01451

  42. Fabbri, M., Brasґo, G., Maugeri, G., Cetintas, O., Gasparini, R., Osep, A., Calderara, S., Leal-Taixe, L., and Cucchiara, R., MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking, Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10829–10839. https://doi.org/10.1109/iccv48922.2021.01067

  43. Makehuman community. Makehuman, 2020. http://www.makehumancommunity.org.

  44. Epic Games Incorporated. Unreal Engine, 2020. https://www.unrealengine.com.

  45. Barbosa, I.B., Cristani, M., Caputo, B., Rognhaugen, A., and Theoharis, T., Looking beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification, arXiv: abs/1701.03153, 2018. https://doi.org/10.1016/j.cviu.2017.12.002

  46. Bak, S., Carr, P., and Lalonde, J., Domain Adaptation through Synthesis for Unsupervised Person Re-identification, arXiv: abs/1804.10094, 2018. https://doi.org/10.1007/978-3-030-01261-812

  47. Sun, X. and Zheng, L., Dissecting Person Re-identification from the Viewpoint of Viewpoint, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 608–617. https://doi.org/10.1109/CVPR.2019.00070

  48. Wang, Y., Liao, S., and Shao, L., Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-identification, Proceedings of the 28th ACM International Conference on Multimedia, 2020. https://doi.org/10.1145/3394171.3413815

  49. Wang, Y., Liang, X., and Liao, S., Cloning Outfits from Real-World Images to 3D Characters for Generalizable Person Re-identification, arXiv: abs/2204.02611, 2022. https://doi.org/10.48550/arXiv.2204.02611

  50. Unity Technologies. 2020. Unity3D: Cross-platform Game Engine. URL: https://unity.com.

  51. Zhong, Z., Zheng, L., Kang G., Li, S., and Yang, Y., Random Erasing Data Augmentation, Proceedings of AAAI, 2020. https://doi.org/10.1609/AAAI.V34I07.7000

  52. Ni, X. and Rahtu, E., FlipReID: Closing the Gap between Training and Inference in Person Reidentification, Proceedings of 2021 9th European Workshop on Visual Information Processing (EUVIP), 2021, pp. 1–6. https://doi.org/10.1109/EUVIP50544.2021.9484010

  53. Li, W., Xu, F., Zhao, J., Zheng, R., Zou, C., Wang, M., and Cheng, Y., HBReID: Harder Batch for Re-identification, arXiv: abs/2112.04761, 2021. https://doi.org/10.48550/arXiv.2112.04761

  54. Huang, Y., Zha, Z., Fu, X., Hong, R., and Li, L., Real-World Person Re-identification via Degradation Invariance Learning, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 14072–14082. https://doi.org/10.1109/cvpr42600.2020.01409

  55. Jiang, Y., Chen, W., Sun, X., Shi, X., Wang, F., and Li, H., Exploring the Quality of GAN Generated Images for Person Re-identification, Proceedings of the 29th ACM International Conference on Multimedia, 2021. https://doi.org/10.1145/3474085.3475547

  56. Wu, C., Ge, W., Wu, A., and Chang, X., Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-identification, arXiv: abs/2203.15210, 2022. https://doi.org/10.48550/arXiv.2203.15210

  57. Wang, G., Lai, J., Huang, P., and Xie, X., Spatial-Temporal Person Re-identification, arXiv:abs/1812.03282, 2019. https://doi.org/10.1609/aaai.v33i01.33018933

  58. Yu, Z., Jin, Z., Wei, L., Guo, J., Huang, J., Cai, D., He, X., and Hua, X., Progressive Transfer Learning for Person Re-identification, IJCAI, 2019. https://doi.org/10.24963/ijcai.2019/586

  59. Sun, Y., Zheng, L., Yang, Y., Tian, Q., and Wang, S., Beyond Part Models: Person Retrieval with Refined Part Pooling, Proceedings of ECCV, 2018. https://doi.org/10.1007/978-3-030-01225-030

  60. Bayoumi, R.M., Hemayed, E.E., Ragab, M.E., and Fayek, M.B., Person Re-identification via Pyramid Multipart Features and Multi-Attention Framework, Big Data and Cognitive Computing, 2022. https://doi.org/10.3390/bdcc6010020

    Book  Google Scholar 

  61. Wang, G., Yang, S., Liu, H., Wang, Z., Yang, Y., Wang, S., Yu, G., Zhou, E., and Sun, J., High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-identification, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 6448–6457. https://doi.org/10.1109/CVPR42600.2020.00648

  62. Sun, K., Xiao, B., Liu, D., and Wang, J., Deep High-Resolution Representation Learning for Human Pose Estimation, Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 5686–5696. https://doi.org/10.1109/CVPR.2019.00584

  63. Yang, J., Zhang, J., Yu, F., Jiang, X., Zhang, M., Sun, X., Chen, Y., and Zheng, W.S., Learning to Know Where to See: A Visibility-Aware Approach for Occluded Person Re-identification, Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 11885–11894

  64. Fang, H., Xie, S., Tai, Y., and Lu, C., RMPE: Regional Multi-person Pose Estimation, Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2353–2362. https://doi.org/10.1109/ICCV.2017.256

  65. Chen, X., Liu, X., Liu, W., Zhang, X., Zhang, Y., and Mei, T., Explainable Person Re-identification with Attribute-guided Metric Distillation, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2022, pp. 11793–11802. https://doi.org/10.1109/ICCV48922.2021.01160

  66. Dai, Y., Sun, Y., Liu, J., Tong, Z., Yang, Y., and Duan, L., Bridging the Source-to-target Gap for Cross-domain Person Re-identification with Intermediate Domains, arXiv: abs/2203.01682, 2022. https://doi.org/10.48550/arXiv.2203.01682

  67. Zhang, H., Cisse, M., Dauphin, Y., and Lopez-Paz, D., Mixup: Beyond Empirical Risk Minimization, arXiv: abs/1710.09412, 2018. https://doi.org/10.48550/arXiv.1710.09412

  68. Huang, X. and Belongie, S.J., Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization, Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1510–1519. https://doi.org/10.1109/ICCV.2017.167

  69. Avola, D., Cascio, M., Cinque, L., Fagioli, A., and Petrioli, C., Person Re-identification through Wi-Fi Extracted Radio Biometric Signatures, IEEE Transactions on Information Forensics and Security, 2022, vol. 17, pp. 1145–1158. https://doi.org/10.1109/TIFS.2022.3158058

    Article  Google Scholar 

  70. Qi, L., Shen, J., Liu, J., Shi, Y., and Geng, X., Label Distribution Learning for Generalizable Multisource Person Re-identification, arXiv: abs/2204.05903, 2022. https://doi.org/10.48550/arXiv.2204.05903

  71. Yang, X., Zhou, Z., Wang, Q., Wang, Z., Li, X., and Li, H., Cross-domain Unsupervised Pedestrian Re-identification Based on Multi-view Decomposition, Multimed Tools Appl., 2022. https://doi.org/10.1007/s11042-021-11797-w

    Book  Google Scholar 

  72. Elharrouss, O., Almaadeed, N., Al-Maadeed, S.A., and Bouridane, A., Gait Recognition for Person Re-identification, J. Supercomput., 2021, no. 77, pp. 3653–3672. https://doi.org/10.1007/s11227-020-03409-5

  73. Chao, H., He, Y., Zhang, J., and Feng, J., GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition, arXiv: abs/1811.06186, 2019. https://doi.org/10.1609/aaai.v33i01.33018126

  74. Jiang, X., Qiao, Y., Yan, J., Li, Q., Zheng, W., and Chen, D., SSN3D: Self-Separated Network to Align Parts for 3D Convolution in Video Person Re-identification, Proceedings of the AAAI Conference on Artificial Intelligence, 2021, no. 35(2), pp. 1691–1699. https://ojs.aaai.org/index.php/AAAI/article/view/16262.

  75. Yang, F., Wang, X., Zhu, X., Liang, B., and Li, W., Relation-Based Global-Partial Feature Learning Network for Video-Based Person Re-identification, Neurocomputing, 2022, vol. 488, pp. 424–435. https://doi.org/10.1016/j.neucom.2022.03.032

    Article  Google Scholar 

  76. Lu, Z., Zhang, G., Huang, G., Yu, Z., Pun, C., and Ling, K., Video Person Re-identification Using Key Frame Screening with Index and Feature Reorganization Based on Inter-frame Relation, Int. J. Mach. Learn. Cyber., 2022. https://doi.org/10.1007/s13042-022-01560-4

  77. Yadav, A. and Vishwakarma, D.K., Person Re-identification Using Deep Learning Networks: A Systematic Review, arXiv: abs/2012.13318, 2020. https://doi.org/10.48550/arXiv.2012.13318

  78. Zhang, Z., Lan, C., Zeng, W., Jin, X., and Chen, Z., Relation-Aware Global Attention for Person Reidentification, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 3183–3192. https://doi.org/10.1109/CVPR42600.2020.00325

  79. Pathak, P., Eshratifar, A.E., and Gormish, M.J., Video Person Re-ID: Fantastic Techniques and Where to Find Them, Proceedings of AAAI, 2020. https://doi.org/10.1609/aaai.v34i10.7219

  80. Liu, X., Zhang, P., Yu, C., Lu, H., and Yang, X., Watching You: Global-guided Reciprocal Learning for Video-based Person Re-identification, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 13329–13338. https://doi.org/10.1109/CVPR46437.2021.01313

  81. Gao, S., Wang, J., Lu, H., and Liu, Z., Pose-Guided Visible Part Matching for Occluded Person ReID, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 11741–11749. https://doi.org/10.1109/cvpr42600.2020.01176

  82. Zhang, S., Yin, Z., Wu, X., Wang, K., Zhou, Q., and Kang, B., FPB: Feature Pyramid Branch for Person Re-identification, arXiv: abs/2108.01901, 2021. https://doi.org/10.48550/arXiv.2108.01901

  83. Yang, F., Li, W., Liang, B., Han, S., and Zhu, X., Multi-stage Attention Network for Video-Based Person Re-identification, IET Comput. Vis., 2022, pp. 1–11. https://doi.org/10.1049/cvi2.1210

  84. Wu, G., Zhu, X., and Gong, Sh., Learning Hybrid Ranking Representation for Person Re-identification, Pattern Recognition, 2022, vol. 121. https://doi.org/10.1016/j.patcog.2021.108239

  85. Zhong, Z., Zheng, L., Cao, D., and Li, S., Re-ranking Person Re-identification with k-Reciprocal Encoding, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 3652–3661. https://doi.org/10.1109/CVPR.2017.389

  86. Bohush, R.P., Ablameyko, S.V., and Adamovskiy, E.R., Image Similarity Estimation Based on Ratio and Distance Calculation between Features, Pattern Recognit. Image Anal., 2020, no. 30, pp. 147–159. https://doi.org/10.1134/S1054661820020030

  87. He, K., Zhang, X., Ren, S., and Sun, J., Deep Residual Learning for Image Recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778. https://doi.org/10.1109/cvpr.2016.90

  88. Choi, S., Kim, T., Jeong, M., Park, H., and Kim, C., Meta Batch-Instance Normalization for Generalizable Person Re-identification, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 3424–3434. https://doi.org/10.1109/CVPR46437.2021.00343

  89. Huang, G., Liu, Z., and Weinberger, K.Q., Densely Connected Convolutional Networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2261–2269. https://doi.org/10.1109/CVPR.2017.243

  90. Chen, P., Dai, P., Liu, J., Zheng, F., Tian, Q., and Ji, R., Dual Distribution Alignment Network for Generalizable Person Re-identification, arXiv: abs/2007.13249, 2021. https://doi.org/10.48550/arXiv.2007.13249

  91. Zhao, C., Chen, K., Wei, Z., Chen, Y., Miao, D., and Wang, W., Multilevel Triplet Deep Learning Model for Person Re-identification, Pattern Recognit. Lett., 2019, no. 117, pp. 161–168. https://doi.org/10.1016/j.patrec.2018.04.029

  92. Yao, Y., Jiang, X., Fujita, H., and Fang, Z., A Sparse Graph Wavelet Convolution Neural Network for Video-Based Person Re-identification, Pattern Recognition, 2022, vol. 129. https://doi.org/10.1016/j.patcog.2022.108708

  93. Lu, P., Lu, K., Wang, W., Zhang, J., Chen, P., and Wang, B., Real-Time Pedestrian Detection in Monitoring Scene Based on Head Model, Intelligent Computing Theories and Application (ICIC 2019), Lecture Notes in Computer Science, vol. 11644, Cham: Springer, 2019, pp. 558–568. https://doi.org/10.1007/978-3-030-26969-2_53

    Book  Google Scholar 

  94. Lee, S., Kang, Q., Madireddy, S., Balaprakash, P., Agrawal, A., Choudhary, A.N., Archibald, R., and Liao, W., Improving Scalability of Parallel CNN Training by Adjusting Mini-batch Size at Run-Time, Proceedings of the 2019 IEEE International Conference on Big Data, 2019, pp. 830–839. https://doi.org/10.1109/BigData47090.2019.9006550

  95. Lewkowycz, A., How to Decay Your Learning Rate, arXiv: abs/2103.12682, 2021. https://doi.org/10.48550/arXiv.2103.12682

  96. Lewkowycz, A., Bahri, Y., Dyer E., Sohl-Dickstein, J., and Gur-Ari, G., The Large Learning Rate Phase of Deep Learning: The Catapult Mechanism, arXiv: abs/2003.02218, 2020. https://doi.org/10.48550/arXiv.2003.02218

  97. Ulyanov, D., Vedaldi, A., and Lempitsky, V.S., Instance Normalization: The Missing Ingredient for Fast Stylization, arXiv: abs/1607.08022, 2016. https://doi.org/10.48550/arXiv.1607.08022

  98. Chen, H., Ihnatsyeva, S., Bohush, R., and Ablameyko, S., Choice of Activation Function in Convolution Neural Network in Video Surveillance Systems, Programming and Computer Software, 2022, no. 5, pp. 312–321. https://doi.org/10.1134/S0361768822050036

  99. Nair, V. and Hinton, G.E., Rectified Linear Units Improve Restricted Boltzmann Machines, Proceedings of the 27th International Conference on Machine Learning (ICML), Haifa, Israel, 2010, pp. 807–814.

  100. Maas, A.L., Hannum, A.Y., and Ng, A.Y., Rectifier Nonlinearities Improve Neural Network Acoustic Models, Proceedings of the 30th International Conference on Machine Learning (ICML), Atlanta, Georgia, 2013

  101. Xu, B., Wang, N., Chen, T., and Li, M., Empirical Evaluation of Rectified Activations in Convolutional Network, arXiv: abs/1505.00853, 2015. https://doi.org/10.48550/arXiv.1505.00853

  102. Clevert, D., Unterthiner, T., and Hochreiter, S., Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs), arXiv: abs/1511.07289v5, 2016. https://doi.org/10.48550/arXiv.1511.07289

  103. Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S., Self-Normalizing Neural Networks, arXiv: abs/1706.02515, 2017. https://doi.org/10.48550/arXiv.1706.02515

  104. Hendrycks, D. and Gimpel, K., Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units, arXiv: abs/1606.08415, 2016. https://doi.org/10.48550/arXiv.1606.08415

  105. Ramachandran, P., Zoph, B., and Le, Q.V., Swish: A Self-Gated Activation Function, arXiv: abs/1710.05941v2, 2017. https://doi.org/10.48550/arXiv.1710.05941

  106. Misra, D., Mish: A Self Regularized Non-Monotonic Neural Activation Function, arXiv: abs/1908. 08681, 2019. https://doi.org/10.48550/arXiv.1908.08681

  107. Lavi, B., Ullah, I., Fatan, M., and Rocha, A., Survey on Reliable Deep Learning-Based Person Reidentification Models: Are We There Yet?, arXiv: abs/2005.00355, 2020. https://doi.org/10.48550/arXiv.2005.00355

  108. Rao, H. and Miao, C., SimMC: Simple Masked Contrastive Learning of Skeleton Representations for Unsupervised Person Re-identification, arXiv: abs/2204.09826v1, 2022. https://doi.org/10.48550/arXiv.2204.09826

  109. Zheng, Y., Zhou, Y., Zhao, J., Jian, M., Yao, R., Liu, B., and Chen, Y., A Siamese Pedestrian Alignment Network for Person Re-identification, Multim. Tools Appl., 2021, no. 80, pp. 33951–33970. https://doi.org/10.1007/s11042-021-11302-3

  110. Zheng, M., Karanam, S., Wu, Z., and Radke, R.J., Re-identification with Consistent Attentive Siamese Networks, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 5728–5737. https://doi.org/10.1109/CVPR.2019.00588

  111. Hermans, A., Beyer, L., and Leibe, B., In Defense of the Triplet Loss for Person Re-Identification, arXiv: abs/1703.07737, 2017. https://doi.org/10.48550/arXiv.1703.07737

  112. Organisciak, D., Riachy, C., Aslam, N., and Shum, H., Triplet Loss with Channel Attention for Person Re-identification, J. WSCG, 2019, no. 27. https://doi.org/10.24132/JWSCG.2019.27.2.9

  113. Zhai, Y., Guo, X., Lu, Y., and Li, H., In Defense of the Classification Loss for Person Re-identification, Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019, pp. 1526–1535. https://doi.org/10.1109/CVPRW.2019.00194

  114. Alex, D., Sami, Z., Banerjee, S., and Panda, S., Cluster Loss for Person Re-identification, Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing, 2018. https://doi.org/10.1145/3293353.3293396

  115. Bai, Z., Wang, Z., Wang, J., Hu, D., and Ding, E., Unsupervised Multi-Source Domain Adaptation for Person Re-identification, Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 12909–12918. https://doi.org/10.1109/CVPR46437.2021.01272

  116. Chen, H., Lagadec, B., and Bremond, F., Unsupervised Lifelong Person Re-identification via Contrastive Rehearsal, arXiv: abs/2203.06468, 2022. https://doi.org/10.48550/arXiv.2203.06468

  117. Zhang, X., Li, D., Wang, Z., Wang, J., Ding, E., Shi, J., Zhang, Z., and Wang, J., Implicit Sample Extension for Unsupervised Person Re-identification, arXiv: abs/2204.06892, 2022. https://doi.org/10.48550/arXiv.2204.06892

  118. Zhu, K., Guo, H., Yan, T., Zhu, Y., Wang, J., and Tang, M., Part-Aware Self-Supervised Pre-Training for Person Re-identification, arXiv: abs/2203.03931, 2022. https://doi.org/10.48550/arXiv.2203.03931

  119. Fu, D., Chen, D., Yang, H., Bao, J., Yuan, L., Zhang, L., Li, H., Wen, F., and Chen, D., Large-Scale Pre-training for Person Re-identification with Noisy Labels, arXiv: abs/2203.16533, 2022. https://doi.org/10.48550/arXiv.2203.16533

  120. Cho, Y.H., Kim, W.J., Hong, S., and Yoon, S., Part-based Pseudo Label Refinement for Unsupervised Person Re-identification, arXiv: abs/2203.14675, 2022. https://doi.org/10.48550/arXiv.2203.14675

  121. Chen, M., Wang, Z., and Zheng, F., Benchmarks for Corruption Invariant Person Re-identification, arXiv: abs/2111.00880, 2021. https://doi.org/10.48550/arXiv.2111.00880

  122. Dataset and Code. https://www.pkuvmc.com/dataset.html.

Download references

Funding

This work is supported by the National High-end Foreign Experts Program (G2021016028L) and Zhejiang Shuren University Basic Scientific Research Special Funds.

Author information

Authors and Affiliations

  1. Zhejiang Shuren University, Hangzhou, Zhejiang, China

    H. Chen

  2. International Science and Technology Cooperation Base of Zhejiang Province: Remote Sensing Image Processing and Application, 310000, Hangzhou, China

    H. Chen

  3. Euphrosyne Polotskaya State University of Polotsk, Polotsk, Belarus

    S. A. Ihnatsyeva & R. P. Bohush

  4. Belarusian State University, Minsk, Belarus

    S. V. Ablameyko

Authors
  1. H. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. A. Ihnatsyeva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. P. Bohush
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. V. Ablameyko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to H. Chen, S. A. Ihnatsyeva, R. P. Bohush or S. V. Ablameyko.

Additional information

This paper was recommended for publication O.P. Kuznetsov, a member of the Editorial Board

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, H., Ihnatsyeva, S.A., Bohush, R.P. et al. Person Re-identification in Video Surveillance Systems Using Deep Learning: Analysis of the Existing Methods. Autom Remote Control 84, 497–528 (2023). https://doi.org/10.1134/S0005117923050041

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0005117923050041

Keywords:

Search

Navigation