Smart City Gnosys

Smart city article details

Title	Frame Extraction Person Retrieval Framework Based On Improved Yolov8S And The Stage-Wise Clustering Person Re-Identification
ID_Doc	27017
Authors	Zhuang J.; Wang N.; Zhuang Y.; Hao Y.
Year	2025
Published	IET Image Processing, 19, 1
DOI	http://dx.doi.org/10.1049/ipr2.70046
Abstract	Person re-identification (Re-ID), a crucial research area in smart city security, faces challenges due to person posture changes, object occlusion and other factors, making it difficult for existing methods to accurately retrieving target person in video surveillance. To resolve this problem, we propose a person retrieval framework that integrates YOLOv8s and person Re-ID. Improved YOLOv8s is employed to extract person categories from the video on a frame-by-frame basis, and when combined with the stage-wise clustering person Re-ID network (SCPN), it enables collaborative person retrieval across multiple cameras. Notably, a feature precision (FP) module is added in the YOLOv8s network to form FP-YOLOv8s, and SCPN incorporates innovative enhancements including the stage-wise learning rate scheduler, centralized clustering loss and adaptive representation joint attention module into the person Re-ID baseline model. Comprehensive experiments on COCO, Market-1501 and DukeMTMC-ReID datasets demonstrate that our proposed framework outperforms several other leading methods. Given the scarcity of image-video person Re-ID datasets, we also provide an extended image-video person (EIVP) dataset, which contains 102 videos and 814 bounding boxes of 57 identities captured by 8 cameras. The video reasoning detection score of this framework reaches 78.8% on this dataset, indicating a 3.2% increase compared to conventional models. © 2025 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Author Keywords	feature representation; frame extraction; person re-identification; person retrieval; YOLOv8s

Similar Articles

Id	Similarity	Authors	Title	Published
4278	0.897	Selvan C.; Anwar Basha H.; Meenakshi K.; Naveen S.	A Review On Person Re-Identification Techniques And Its Analysis	IEEE Access, 13 (2025)
51477	0.887	Perwaiz N.; Fraz M.M.; Shahzad M.	Smart Surveillance With Simultaneous Person Detection And Re-Identification	Multimedia Tools and Applications, 83, 5 (2024)
7827	0.873	Maqsood M.; Yasmin S.; Gillani S.; Bukhari M.; Rho S.; Yeo S.-S.	An Efficient Deep Learning-Assisted Person Re-Identification Solution For Intelligent Video Surveillance In Smart Cities	Frontiers of Computer Science, 17, 4 (2023)
5185	0.871	Li Q.; Hu W.-Y.; Li J.-Y.; Liu Y.; Li M.-X.	A Survey Of Person Re-Identification Based On Deep Learning; [基于深度学习的行人重识别方法综述]	Gongcheng Kexue Xuebao/Chinese Journal of Engineering, 44, 5 (2022)
44581	0.868	Zhang Y.; Yang H.; Zhang Y.; Dou Z.; Liao S.; Zheng W.; Zhang S.; Ye M.; Yan Y.; Li J.; Wang S.	Recent Progress In Person Re-Id; [行人再识别技术研究进展]	Journal of Image and Graphics, 28, 6 (2023)
41890	0.86	Liu J.; Lin M.; Zhao M.; Zhan C.; Li B.; Chui J.K.T.	Person Re-Identification Via Semi-Supervised Adaptive Graph Embedding	Applied Intelligence, 53, 3 (2023)
7876	0.859	Wang Z.; Luo S.; Sun H.; Pan H.; Yin J.	An Efficient Non-Local Attention Network For Video-Based Person Re-Identification	ACM International Conference Proceeding Series (2019)
39446	0.858	Cui L.; Liu M.; Wang R.; Zafar N.	Novel Attention-Based Framework For Person Re-Identification In Video Surveillance	Lecture Notes in Electrical Engineering, 1209 LNEE (2024)
28943	0.857	Li Y.; Lian G.; Zhang W.; Ma G.; Ren J.; Yang J.	Heterogeneous Feature-Aware Transformer-Cnn Coupling Network For Person Re-Identification	PeerJ Computer Science, 8 (2022)
46058	0.857	Qammar J.; Ahmad W.	Resolution Representation Based Person Re-Identification For Smart Cities Using Deep Neural Networks (Dnns)	2021 International Conference on Information Technology, ICIT 2021 - Proceedings (2021)