TY  - CONF
EP  - 135
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149140015&doi=10.1109%2fICFTSC57269.2022.10039913&partnerID=40&md5=a65fb98c4e4c2ba333002ad6cf710834
A1  - Amosa, T.I.
A1  - Sebastian, P.
A1  - Izhar, L.I.
A1  - Ibrahim, O.
SN  - 9798350334548
PB  - Institute of Electrical and Electronics Engineers Inc.
Y1  - 2022///
KW  - Computer vision; Deep learning
KW  -  Deep learning; Identification modeling; Illumination-adaptive; Large-scale datasets; Performance; Person re identifications; Re identifications; Synthetic datasets; Visual; Visual condition
KW  -  Large dataset
SP  - 130
ID  - scholars17241
TI  - Investigating the Impact of Illumination and Viewpoint Variations on Transformer-based Person Re-Identification
N1  - cited By 0; Conference of 2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022 ; Conference Date: 1 December 2022 Through 2 December 2022; Conference Code:186671
N2  - Variations in visual factors such as illumination, viewpoint, resolution, background, pose, and so on are commonly regarded as significant issues in object re-identification (re-ID). Despite widespread recognition of their importance in determining the performance of an object re-ID model, not enough attention is paid to how these factors affect re-ID systems. One of the major impediments to investigating how these factors affect the performance of re-ID models is the lack of datasets with unbiased distribution of these difficult visual conditions. To make up for the lack of large-scale datasets with a balanced distribution of such photometric and geometric transforms, recent studies suggest using game engines to generate synthetic datasets. This study proposes a quantitative investigation of the impact of two critical visual factors: illumination and Tranfomer-based re-ID models on synthetic dataset. © 2022 IEEE.
AV  - none
ER  -