KSII Transactions on Internet and Information Systems (TIIS)

  • 제15권12호
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  • Pages.4456-4475
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  • 2021
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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A new framework for Person Re-identification: Integrated level feature pattern (ILEP)

  • Manimaran, V. (Department of Information Technology, National Engineering College) ;
  • Srinivasagan, K.G. (Department of Information Technology, National Engineering College) ;
  • Gokul, S. (Language Science and Technology, SaarlandUniversity) ;
  • Jacob, I.Jeena (Department of Computer Science and Engineering, Gitam University) ;
  • Baburenagarajan, S. (Department of Computer Science and Engineering, PET Engineering College)
  • 투고 : 2021.07.20
  • 심사 : 2021.09.23
  • 발행 : 2021.12.31
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초록

The system for re-identifying persons is used to find and verify the persons crossing through different spots using various cameras. Much research has been done to re-identify the person by utilising features with deep-learned or hand-crafted information. Deep learning techniques segregate and analyse the features of their layers in various forms, and the output is complex feature vectors. This paper proposes a distinctive framework called Integrated Level Feature Pattern (ILFP) framework, which integrates local and global features. A new deep learning architecture named modified XceptionNet (m-XceptionNet) is also proposed in this work, which extracts the global features effectively with lesser complexity. The proposed framework gives better performance in Rank1 metric for Market1501 (96.15%), CUHK03 (82.29%) and the newly created NEC01 (96.66%) datasets than the existing works. The mean Average Precision (mAP) calculated using the proposed framework gives 92%, 85% and 98%, respectively, for the same datasets.

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