A new framework for Person Re-identification: Integrated level feature pattern (ILEP)

• V.Manimaran,
• K.G.Srinivasagan,
• S.Gokul,
• I.Jeena Jacob,
• S.Baburenagarajan,

Vol. 15, No. 12, December 31, 2021
10.3837/tiis.2021.12.011, Download Paper (Free):

• Person reidentification
• LBP
• HOG
• Deep Features
• PCA
• CNN
• m-XceptionNet

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.

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