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

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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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Acknowledgements

This paper was supported by Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (P0008703, The Competency Development Program for Industry Specialist) and also the MSIT (Ministry of Science and ICT), Republic of Korea, under the ITRC (Information Technology Research Center) support program (IITP-2022-2018-0-01799) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).

Author information

Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, 43600, Pakistan

    Muazzam Maqsood, Sadaf Yasmin & Maryam Bukhari

  2. Department of Computer Science, Bahria University, Lahore, 54600, Pakistan

    Saira Gillani

  3. Department of Industrial Security, Chung-Ang University, Seoul, 06974, Republic of Korea

    Seungmin Rho

  4. Department of Computer Engineering, Mokwon University, Daejeon, 35349, Republic of Korea

    Sang-Soo Yeo

Authors
  1. Muazzam Maqsood
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  2. Sadaf Yasmin
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  3. Saira Gillani
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  4. Maryam Bukhari
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  5. Seungmin Rho
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  6. Sang-Soo Yeo
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Corresponding authors

Correspondence to Seungmin Rho or Sang-Soo Yeo.

Additional information

Muazzam Maqsood is serving as an Assistant Professor at the Department of Computer Science, COMSATS University Islamabad, Attock Campus, Pakistan. He holds a PhD in software engineering with a keen interest in artificial intelligence and deep learning-based systems. His main research focus is to use the latest machine learning and deep learning algorithms to develop automated solutions, especially in the field of pattern recognition and data analytics. He has published various top-ranked impact factor papers in the area of image processing, medical imaging, recommender systems, stock exchange prediction, and big data analytics. He is also a reviewer of many impact factor journals and a program committee member of various international conferences.

Sadaf Yasmin is currently working as Assistant Professor at the Department of Computer Science, COMSATS University Islamabad, Attock Campus, Pakistan. She has completed her MS and PhD in Computer Science from Capital University of Science and Technology, Pakistan, and BS in Software Engineering from (APCOMS) NUML Islamabad, Pakistan. She has worked on several research projects during and after her PhD She is also serving as a reviewer for various reputed journals. Her research interests include network protocol design, computer vision, medical imaging, and pattern recognition.

Saira Gillani received her PhD degree in Information Sciences from Corvinus University of Budapest, Hungary. She joined the COMSATS Institute of Information Technology, Pakistan in 2016. She also served as an assistant professor in Saudi Electronic University, Saudi Arabia. She is currently serving as an associate professor in Bahria University Lahore, Pakistan. Previously, she worked as research scholar in Corvinno, Technology Transfer Center of Information Technology and Services in Budapest, Hungary and also worked as research associate in CoReNet (Center of Research in Networks and Telecom), CUST, Pakistan. Her areas of interest include data sciences, text mining, data mining, machine learning, vehicular networks, mobile edge computing and Internet of Things.

Maryam Bukhari is perusing her MS degree at COMSATS University Islamabad, Attock Campus, Pakistan. Her research areas include machine learning and image processing.

Seungmin Rho is currently an associate professor at Department of Industrial Security at Chung-Ang University, Republic of Korea. His current research interests include database, big data analysis, music retrieval, multimedia systems, machine learning, knowledge management as well as computational intelligence. He has published 300 papers in refereed journals and conference proceedings in these areas. He has been involved in more than 20 conferences and workshops as various chairs and more than 30 conferences/workshops as a program committee member. He has edited a number of international journal special issues as a guest editor, such as multimedia systems, information fusion, and engineering applications of artificial intelligence.

Sang-Soo Yeo received a PhD degree in Computer Science & Engineering from Chung-Ang University, Republic of Korea in 2005. He is a professor at the Department of Computer Engineering, Mokwon University, Republic of Korea. He worked for MOIS, Ministry of Interior and Safety and worked for PIPC, Personal Information Protection Commission, Republic of Korea from Feb. 2020 to Jul. 2021. He is President of the Institution of Creative Research Professionals (ICRP), and Vice President of ICT Platform Society (ICTPS). He is serving as Steering Chair of the PlatCon conference series, a very comprehensive conference series on platform technology and services. Dr. Yeo’s research interests include security, privacy, personal information Protection, ubiquitous computing, multimedia service, ubiquitous computing, embedded system, and bioinformatics.

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

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Maqsood, M., Yasmin, S., Gillani, S. et al. An efficient deep learning-assisted person re-identification solution for intelligent video surveillance in smart cities. Front. Comput. Sci. 17, 174329 (2023). https://doi.org/10.1007/s11704-022-2050-4

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