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http://dx.doi.org/10.3837/tiis.2021.12.011

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)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.15, no.12, 2021 , pp. 4456-4475 More about this Journal
Abstract
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.
Keywords
Person reidentification; LBP; HOG; Deep features; PCA; CNN; m-XceptionNet;
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