State and Development of Human Re-Identification Technologies in Intelligent Video Systems
Keywords:
Ключові слова: повторна ідентифікація людини, інтелектуальні відеосистеми, комп’ютерний зір, глибоке навчання, ознаковий простір, метричне навчання, генеративні змагальні мережі, attention-механізм (механізм уваги)., Keywords: person re-identification, intelligent video systems, computer vision, deep learning, feature space, metric learning, generative adversarial networks, attention mechanism.
Abstract
Abstract. The article discusses the current state and main directions of development of human re-identification (re-ID) technologies in intelligent video surveillance systems. The formal statement of the re-ID problem is defined and its role in ensuring continuous automatic monitoring of objects between non-overlapping cameras is characterized. The main challenges that complicate the solution of this problem in real conditions are analyzed, including variability of appearance, changes in angle and lighting, partial overlap, domain shift, limited training data and hardware resources. The paper summarizes the approaches to feature extraction, image matching, and deep learning methods used to build effective re-ID models. Particular attention is paid to current trends in the use of generative adversarial networks (GANs) and attention mechanisms that improve identification accuracy. Promising areas for further research, in particular in the field of multimodal learning, adaptation to new domains, and ethical implementation of technologies in public environments, are presented.
References
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26. N. Gheissari, T. B. Sebastian, and R. Hartley, "Person Reidentification Using Spatiotemporal Appearance," in Proc. 2006 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR), vol. 2, New York, NY, USA, 2006, pp. 1528–1535. doi: 10.1109/CVPR.2006.223.
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34. L. Wei, S. Zhang, W. Gao, and Q. Tian, Person Transfer GAN to Bridge Domain Gap for Person Re-Identification, arXiv preprint arXiv:1711.08565, 2018. [Online]. Available: https://arxiv.org/abs/1711.08565
35. S. Liao, Y. Hu, X. Zhu, and S. Z. Li, Person Re-identification by Local Maximal Occurrence Representation and Metric Learning, arXiv preprint arXiv:1406.4216, 2015. [Online]. Available: https://arxiv.org/abs/1406.4216
36. S. Li, S. Bak, P. Carr, and X. Wang, Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification, arXiv preprint arXiv:1803.09882, 2018. [Online]. Available: https://arxiv.org/abs/1803.09882
37. R. Li, X. Yuan, W. Liu, and X. Xu, "Event-based Video Person Re-identification via Cross-Modality and Temporal Collaboration," in Proc. ICASSP 2025 – IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India, 2025, pp. 1–5. doi: 10.1109/ICASSP49660.2025.10889628
2. A. Angelova, A. Krizhevsky, and V. Vanhoucke, "Pedestrian detection with a Large-Field-Of-View deep network," in Proc. 2015 IEEE Int. Conf. Robotics and Automation (ICRA), Seattle, WA, USA, 2015, pp. 704–711. doi: 10.1109/ICRA.2015.7139256.
3. M. E. Chasmai and T. Banerjee, Person Re-Identification, arXiv preprint arXiv:2204.13158, 2022. [Online]. Available: https://arxiv.org/abs/2204.13158
4. H. Huang, D. Li, Z. Zhang, X. Chen, and K. Huang, "Adversarially Occluded Samples for Person Re-identification," in Proc. 2018 IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, 2018, pp. 5098–5107. doi: 10.1109/CVPR.2018.00535.
5. L. Ren, J. Lu, J. Feng, and J. Zhou, "Uniform and Variational Deep Learning for RGB-D Object Recognition and Person Re-Identification," IEEE Transactions on Image Processing, vol. 28, no. 10, pp. 4970–4983, Oct. 2019. doi: 10.1109/TIP.2019.2915655.
6. J. Lin, L. Ren, J. Lu, J. Feng, and J. Zhou, "Consistent-Aware Deep Learning for Person Re-identification in a Camera Network," in Proc. 2017 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 3396–3405.
7. L. Bazzani, M. Cristani, A. Perina, M. Farenzena, and V. Murino, "Multiple-Shot Person Re-identification by HPE Signature," in Proc. 2010 20th Int. Conf. on Pattern Recognition (ICPR), Istanbul, Turkey, 2010, pp. 1413–1416. doi: 10.1109/ICPR.2010.349.
8. W. Li, X. Zhu, and S. Gong, Harmonious Attention Network for Person Re-Identification, arXiv preprint arXiv:1802.08122, 2018. [Online]. Available: https://arxiv.org/abs/1802.08122
9. Y. Yang, J. Yang, J. Yan, S. Liao, D. Yi, and S. Z. Li, "Salient Color Names for Person Re-identification," in Computer Vision – ECCV 2014, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds. Cham: Springer, 2014, pp. 536–551. doi: 10.1007/978-3-319-10590-1_35.
10. Y. Shen, W. Lin, J. Yan, M. Xu, J. Wu, and J. Wang, Person Re-identification with Correspondence Structure Learning, arXiv preprint arXiv:1504.06243, 2015. [Online]. Available: https://arxiv.org/abs/1504.06243
11. D. S. Cheng, M. Cristani, M. Stoppa, L. Bazzani, and V. Murino, "Custom Pictorial Struc-tures for Re-identification," in Proc. British Machine Vision Conference (BMVC), Dundee, UK, 2011.
12. D. Gray and H. Tao, "Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features," in Computer Vision – ECCV 2008, D. Forsyth, P. Torr, and A. Zisserman, Eds. Berlin, Heidelberg: Springer, 2008, pp. 262–275. doi: 10.1007/978-3-540-88682-2_21.
13. T. Matsukawa, T. Okabe, E. Suzuki, and Y. Sato, "Hierarchical Gaussian Descriptor for Person Re-identification," in Proc. 2016 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 1363–1372. doi: 10.1109/CVPR.2016.152.
14. W. R. Schwartz and L. S. Davis, "Learning Discriminative Appearance-Based Models Using Partial Least Squares," in Proc. 2009 XXII Brazilian Symp. on Computer Graphics and Image Processing (SIBGRAPI), Rio de Janeiro, Brazil, 2009, pp. 322–329. doi: 10.1109/SIBGRAPI.2009.42.
15. Z. Lin and L. S. Davis, "Learning Pairwise Dissimilarity Profiles for Appearance Recognition in Visual Surveillance," in Advances in Visual Computing, G. Bebis et al., Eds. Berlin, Heidelberg: Springer, 2008, pp. 23–34. doi: 10.1007/978-3-540-89639-5_3.
16. A. Mignon and F. Jurie, "PCCA: A new approach for distance learning from sparse pairwise constraints," in Proc. 2012 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA, 2012, pp. 2666–2672. doi: 10.1109/CVPR.2012.6247987.
17. S. Pedagadi, J. Orwell, S. Velastin, and B. Boghossian, "Local Fisher Discriminant Analysis for Pedestrian Re-identification," in Proc. 2013 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA, 2013, pp. 3318–3325. doi: 10.1109/CVPR.2013.426.
18. B. J. Prosser, W. Zheng, S. Gong, and T. Xiang, "Person Re-Identification by Support Vector Ranking," in Proc. British Machine Vision Conference (BMVC), Aberystwyth, UK, 2010.
19. D. Yi, Z. Lei, and S. Z. Li, Deep Metric Learning for Practical Person Re-Identification, arXiv preprint arXiv:1407.4979, 2014. [Online]. Available: https://arxiv.org/abs/1407.4979
20. R. R. Varior, M. Haloi, and G. Wang, "Gated Siamese Convolutional Neural Network Architecture for Human Re-identification," in Computer Vision – ECCV 2016, B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds. Cham: Springer, 2016, pp. 791–808. doi: 10.1007/978-3-319-46484-8_48.
21. R. R. Varior, B. Shuai, J. Lu, D. Xu, and G. Wang, A Siamese Long Short-Term Memory Architecture for Human Re-Identification, arXiv preprint arXiv:1607.08381, 2016. [Online]. Available: https://arxiv.org/abs/1607.08381
22. J. You, A. Wu, X. Li, and W.-S. Zheng, "Top-Push Video-Based Person Re-identification," in Proc. 2016 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 1345–1353. doi: 10.1109/CVPR.2016.150.
23. D. Cheng, Y. Gong, S. Zhou, J. Wang, and N. Zheng, "Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function," in Proc. 2016 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 1335–1344. doi: 10.1109/CVPR.2016.149.
24. S. Ding, L. Lin, G. Wang, and H. Chao, Deep Feature Learning with Relative Distance Comparison for Person Re-identification, arXiv preprint arXiv:1512.03622, 2015. [Online]. Available: https://arxiv.org/abs/1512.03622
25. Z. Zheng, L. Zheng, and Y. Yang, Unla-beled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro, arXiv preprint arXiv:1701.07717, 2017. [Online]. Available: https://arxiv.org/abs/1701.07717
26. N. Gheissari, T. B. Sebastian, and R. Hartley, "Person Reidentification Using Spatiotemporal Appearance," in Proc. 2006 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR), vol. 2, New York, NY, USA, 2006, pp. 1528–1535. doi: 10.1109/CVPR.2006.223.
27. Y. Yan, B. Ni, Z. Song, C. Ma, Y. Yan, and X. Yang, Person Re-Identification via Recurrent Feature Aggregation, arXiv preprint arXiv:1701.06351, 2017. [Online]. Available: https://arxiv.org/abs/1701.06351
28. D. A. Vaquero, R. S. Feris, D. Tran, L. Brown, A. Hampapur, and M. Turk, "Attribute-based people search in surveillance environments," in Proc. 2009 Workshop on Applications of Computer Vision (WACV), Snowbird, UT, USA, 2009, pp. 1–8. doi: 10.1109/WACV.2009.5403131.
29. M. Munaro, A. Fossati, A. Basso, E. Menegatti, and L. Van Gool, "One-Shot Person Re-identification with a Consumer Depth Camera," in Person Re-Identification, S. Gong, M. Cristani, S. Yan, and C. C. Loy, Eds. London: Springer, 2014, pp. 161–181. doi: 10.1007/978-1-4471-6296-4_8.
30. A. Haque, A. Alahi, and L. Fei-Fei, Recurrent Attention Models for Depth-Based Person Identification, arXiv preprint arXiv:1611.07212, 2016. [Online]. Available: https://arxiv.org/abs/1611.07212
31. O. Javed, K. Rasheed, M. Shafique, and M. Shah, "Tracking across multiple cameras with disjoint views," in Proc. Ninth IEEE Int. Conf. on Computer Vision (ICCV), vol. 2, Nice, France, 2003, pp. 952–957. doi: 10.1109/ICCV.2003.1238451.
32. I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative Adversarial Networks, arXiv preprint arXiv:1406.2661, 2014. [Online]. Available: https://arxiv.org/abs/1406.2661
33. J. Liu, B. Ni, Y. Yan, P. Zhou, S. Cheng, and J. Hu, "Pose Transferrable Person Re-Identification," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, June 2018.
34. L. Wei, S. Zhang, W. Gao, and Q. Tian, Person Transfer GAN to Bridge Domain Gap for Person Re-Identification, arXiv preprint arXiv:1711.08565, 2018. [Online]. Available: https://arxiv.org/abs/1711.08565
35. S. Liao, Y. Hu, X. Zhu, and S. Z. Li, Person Re-identification by Local Maximal Occurrence Representation and Metric Learning, arXiv preprint arXiv:1406.4216, 2015. [Online]. Available: https://arxiv.org/abs/1406.4216
36. S. Li, S. Bak, P. Carr, and X. Wang, Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification, arXiv preprint arXiv:1803.09882, 2018. [Online]. Available: https://arxiv.org/abs/1803.09882
37. R. Li, X. Yuan, W. Liu, and X. Xu, "Event-based Video Person Re-identification via Cross-Modality and Temporal Collaboration," in Proc. ICASSP 2025 – IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India, 2025, pp. 1–5. doi: 10.1109/ICASSP49660.2025.10889628
Published
2025-10-03
Section
Artificial Intellect Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
