Smart City Gnosys

Smart city article details

Title	Resolution Representation Based Person Re-Identification For Smart Cities Using Deep Neural Networks (Dnns)
ID_Doc	46058
Authors	Qammar J.; Ahmad W.
Year	2021
Published	2021 International Conference on Information Technology, ICIT 2021 - Proceedings
DOI	http://dx.doi.org/10.1109/ICIT52682.2021.9491740
Abstract	The advancement of deep learning has facilitated rapid progress in person re-identification (re-id) task. Its applications in intelligent video surveillance made it a key component of today's smart cities infrastructure. Person re-id task is aimed to identify the person in distributed camera setup with non-overlapping views. The feature extraction process is an important part of person re-id technique. The present state of art methods mostly used ResNet as a backbone for feature extraction, which results in low geometric transformation modeling and low-resolution representation learning. We addressed these two major challenges by integrating deformable convolution module to enhance the transformation modeling capability and replaced the traditional ResNet backbone for person re-id with the novel feature extraction network named as HRNet, which is based on high-resolution representation learning without any additional supervision. The verification of our approach performance is done by conducting an experiment on a person re-id dataset named Market-1501. We achieved 90.57% Rank-1 accuracy and 75.43% mAP, outperforming the ResNet baseline results, which confirmed the effectiveness of our approach and will have a promising future in person re-id. © 2021 IEEE.
Author Keywords	convolution neural networks; deep learning; intelligent monitoring; Person re-identification

Similar Articles

Id	Similarity	Authors	Title	Published
5185	0.915	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)
7827	0.914	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)
28943	0.896	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)
3484	0.89	Yaqoob I.; Hassan M.U.; Niu D.; Zhao X.; Hameed I.A.; Hassan S.-U.	A Novel Person Re-Identification Network To Address Low-Resolution Problem In Smart City Context	ICT Express, 9, 5 (2023)
18110	0.889	Khan S.U.; Hussain T.; Ullah A.; Baik S.W.	Deep-Reid: Deep Features And Autoencoder Assisted Image Patching Strategy For Person Re-Identification In Smart Cities Surveillance	Multimedia Tools and Applications, 83, 5 (2024)
4278	0.886	Selvan C.; Anwar Basha H.; Meenakshi K.; Naveen S.	A Review On Person Re-Identification Techniques And Its Analysis	IEEE Access, 13 (2025)
30088	0.884	Qian Y.; Barthelemy J.; Karuppiah E.; Perez P.	Identifying Re-Identification Challenges: Past, Current And Future Trends	SN Computer Science, 5, 7 (2024)
10212	0.877	Mamedov T.; Kuplyakov D.; Konushin A.	Approaches To Improve The Quality Of Person Re-Identification For Practical Use	Sensors, 23, 17 (2023)
41889	0.874	Qi L.; Ren Z.; Liu J.; Geng X.	Person Re-Identification Method Based On Hybrid Real-Synthetic Data; [虚实结合的行人重识别方法]	Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 62, 2 (2025)
925	0.873	Sun Z.; Wang X.; Zhang Y.; Song Y.; Zhao J.; Xu J.; Yan W.; Lv C.	A Comprehensive Review Of Pedestrian Re-Identification Based On Deep Learning	Complex and Intelligent Systems, 10, 2 (2024)