SSRScientific and Social Research2661-43322981-9946Bio-Byword Scientific Publishing Pty. Ltd.10.26689/ssr.v6i3.6298ArticleAn Overview of Re-Identificationhttps://artdesignp.com/journal/SSR/6/3/10.26689/ssr.v6i3.6298ZhaoPeinan,ZhouHongyu2024632024-03-27Pedestrian re-identification (Re-ID) is an emerging cutting-edge technology in the research area of intelligentvideo analysis in recent years, belonging to the category of image processing and analysis in complex video environments. This technology plays a crucial role in numerous monitoring and security applications and has been a hot topic in computer vision research. Pedestrian re-identification is considered an important sub-problem in image retrieval, which involves using computer vision algorithms to match pedestrian images or videos across devices to identify the same pedestrian from image databases of different monitoring devices. Research in this area can be traced back to the 1990s when researchers proposed various methods to address the challenge of pedestrian re-identification. This article summarizes the relevant research on personnel re-identification based on deep learning technology and its applications in different scenarios. Besides, it also identifies existing problems with this technology and its prospects.

Su C, Li J, Zhang S, et al., 2017, Pose-Driven Deep Convolutional Model for Person Re-identification. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), 3980–3989. https://www.doi.org/10.1109/ICCV.2017.427

He L, Liang J, Li H, et al., 2018, Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-free Approach. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7073–7082, https://www.doi.org/10.1109/CVPR.2018.00739.

Li JB, Li XW, Liu HZ, et al., 2022, Person Re-Identification Based on Local Feature Relation and Global Attention Mechanism. Computer Engineering,48(1): 245–252.

Xu S, Liu Q, Shi Y, et al., 2022, Person Re-Identification Based on Diversified Local Attention Network. Journal of Electronics & Information Technology, 44(1): 211–220. https://www.doi.org/10.11999/JEIT201003

Wang G, Zhang T, Cheng J, et al., 2019, RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment. Proceedings of the International Conference on Computer Vision, 3623–3632.

Zheng AH, Zeng XQ, Jiang B, et al., 2020, Cross-Modal Person Re-Identification Based on Local Heterogeneous Collaborative Dual-Path Network. Pattern Recognition and Artificial Intelligence, 33(10): 867–878.

Hafner FM, Bhuyian A, Kooij JFP, et al. 2022, Cross-Modal Distillation For RGB-Depth Person Re-Identification. Computer Vision and Image Understanding, 216: 103352.

Liu J, Zha ZJ, Chen D, et al., 2019, Adaptive Transfer Network for Cross-Domain Person Re-Identification, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2019, 7195–7204, https://www.doi.org/10.1109/CVPR.2019.00737

Shi X, Wu T, 2022, Person Re-Identification Algorithm Adapted to Cross-Domain. Journal of Shenyang Aerospace University, 39(6): 46–56.

Li G, Qu H, Zhu C, et al., 2023, Domain Adapted Person Re-Identification Algorithm Based on Joint Network. Computer and Modernization, 2023(6): 48–55.

Yan Y, Cheng Q, Li X, et al., 2023, Cross-view Person Re-identification Based on Joint Dictionary Pair Learning. Software Guide, 22(05): 198–205. https://www.doi.org/10.11907/rjdk.221483

Huang H, Tao W, Du T, 2023, Review of Pedestrian Re-identification Based on Metric Learning. Journal of Shenyang Ligong University, 42(05): 1–10+17.

Xu S, Cheng Y, Gu K, et al., 2017, Jointly Attentive Spatial-Temporal Pooling Networks for Video-Based Person Re-Identification. 2017 IEEE International Conference on Computer Vision (ICCV), 4743–4752. https://www.doi.org/10.1109/ICCV.2017.507

Liu YQ, Ma BP, 2023, Sketch images-guided clothes-changing person re-identification. Journal of Image and Graphics, 28(5): 1396–1408.

Wang Y-H, Zhu Y, Tan T-N, 2002, Biometrics Personal Identification Based on Iris Pattern. Acta Automatica Sinica, 28(1): 1–10.

Somers V, Vleeschouwer CD, Alahi A, 2023, Body Part-Based Representation Learning for Occluded Person Re-Identification, Proceedings of the 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 1613–1623. https://www.doi.org/10.1109/WACV56688.2023.00166

Wu C, Ge W, Wu A, et al., Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-Identification. Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 20206–20216. https://www.doi.org/10.1109/CVPR52688.2022.01960

You M, Yin Y, Xie L, et al., 2021, User Profiling Based on Activity Sensing. Journal of Zhejiang University (Engineering Science), 55(4): 608–614.

Xue X, Yang Z, 2023, Automatic Monitoring Method of Public Library User Behavior Based on Face Recognition Technology. Control Theory and Applications, 42(8): 28–33.

Li H, 2022, Research on the Design of Home Service Robot Based on Slam Navigation and Face Recognition. Information & Computer, 34(13): 127–130.