TIIS (한국인터넷정보학회)
Current Result Document : 4 / 4
| 한글제목(Korean Title) |
A new framework for Person Re-identification: Integrated level feature pattern (ILEP) |
| 영문제목(English Title) |
A new framework for Person Re-identification: Integrated level feature pattern (ILEP) |
| 저자(Author) |
V.Manimaran
K.G.Srinivasagan
S.Gokul
I.Jeena Jacob
S.Baburenagarajan
|
| 원문수록처(Citation) |
VOL 15 NO. 12 PP. 4456 ~ 4475 (2021. 12) |
한글내용
(Korean Abstract) |
|
영문내용
(English 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. |
| 키워드(Keyword) |
Person reidentification
LBP
HOG
Deep features
PCA
CNN
m-XceptionNet
|
| 파일첨부 |
PDF 다운로드
|
