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An efficient deep learning-assisted person re-identification solution for intelligent video surveillance in smart cities

Authors:

Muazzam Maqsood,

Maryam Bukhari,

Sang-Soo YeoAuthors Info & Claims

Frontiers of Computer Science, Volume 17, Issue 4

https://doi.org/10.1007/s11704-022-2050-4

Published: 12 December 2022 Publication History

Abstract

Innovations on the Internet of Everything (IoE) enabled systems are driving a change in the settings where we interact in smart units, recognized globally as smart city environments. However, intelligent video-surveillance systems are critical to increasing the security of these smart cities. More precisely, in today’s world of smart video surveillance, person re-identification (Re-ID) has gained increased consideration by researchers. Various researchers have designed deep learning-based algorithms for person Re-ID because they have achieved substantial breakthroughs in computer vision problems. In this line of research, we designed an adaptive feature refinement-based deep learning architecture to conduct person Re-ID. In the proposed architecture, the inter-channel and inter-spatial relationship of features between the images of the same individual taken from nonidentical camera viewpoints are focused on learning spatial and channel attention. In addition, the spatial pyramid pooling layer is inserted to extract the multiscale and fixed-dimension feature vectors irrespective of the size of the feature maps. Furthermore, the model’s effectiveness is validated on the CUHK01 and CUHK02 datasets. When compared with existing approaches, the approach presented in this paper achieves encouraging Rank 1 and 5 scores of 24.6% and 54.8%, respectively.

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Cited By

Meng YCheng HWang ZZhu HLao XZhang Y(2024)Efficient Multi-modal Human-Centric Contrastive Pre-training with a Pseudo Body-Structured PriorPattern Recognition and Computer Vision10.1007/978-981-97-8620-6_8(114-128)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1007/978-981-97-8620-6_8
Ahmed ULin J(2023)RETRACTED ARTICLE: Robust adversarial uncertainty quantification for deep learning fine-tuningThe Journal of Supercomputing10.1007/s11227-023-05087-579:10(11355-11386)Online publication date: 25-Feb-2023
https://dl.acm.org/doi/10.1007/s11227-023-05087-5

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An efficient deep learning-assisted person re-identification solution for intelligent video surveillance in smart cities

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Information & Contributors

Information

Published In

cover image Frontiers of Computer Science: Selected Publications from Chinese Universities

Frontiers of Computer Science: Selected Publications from Chinese Universities Volume 17, Issue 4

Aug 2023

221 pages

ISSN:2095-2228

EISSN:2095-2236

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© Higher Education Press 2023.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 12 December 2022

Accepted: 06 July 2022

Received: 25 January 2022

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Cited By

Meng YCheng HWang ZZhu HLao XZhang Y(2024)Efficient Multi-modal Human-Centric Contrastive Pre-training with a Pseudo Body-Structured PriorPattern Recognition and Computer Vision10.1007/978-981-97-8620-6_8(114-128)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1007/978-981-97-8620-6_8
Ahmed ULin J(2023)RETRACTED ARTICLE: Robust adversarial uncertainty quantification for deep learning fine-tuningThe Journal of Supercomputing10.1007/s11227-023-05087-579:10(11355-11386)Online publication date: 25-Feb-2023
https://dl.acm.org/doi/10.1007/s11227-023-05087-5

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