Improving CNN-based Person Re-identification using score Normalization

Person re-identification (PRe-ID) is a crucial task in security, surveillance, and retail analysis, which involves identifying an individual across multiple cameras and views. However, it is a challenging task due to changes in illumination, background, and viewpoint. Efficient feature extraction an...

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Bibliographic Details
Published in2023 IEEE International Conference on Image Processing (ICIP) pp. 2890 - 2894
Main Authors Chouchane, Ammar, Ouamane, Abdelmalik, Himeur, Yassine, Mansoor, Wathiq, Atalla, Shadi, Benzaibak, Afaf, Boudellal, Chahrazed
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.10.2023
Subjects
Cameras
CNN
Feature extraction
Iron
Lighting
Measurement
PRe-ID
Score Normalization
Security
Surveillance
XQDA
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Summary:Person re-identification (PRe-ID) is a crucial task in security, surveillance, and retail analysis, which involves identifying an individual across multiple cameras and views. However, it is a challenging task due to changes in illumination, background, and viewpoint. Efficient feature extraction and metric learning algorithms are essential for a successful PRe-ID system. This paper proposes a novel approach for PRe-ID, which combines a Convolutional Neural Network (CNN) based feature extraction method with Cross-view Quadratic Discriminant Analysis (XQDA) for metric learning. Additionally, a matching algorithm that employs Mahalanobis distance and a score normalization process to address inconsistencies between camera scores is implemented. The proposed approach is tested on four challenging datasets, including VIPeR, GRID, CUHK01, and PRID450S. The proposed approach has demonstrated its effectiveness through promising results obtained from the four challenging datasets.
DOI:10.1109/ICIP49359.2023.10222062