English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Person re-identification (PRe-ID) is a crucial task in security,surveillance, and retail analysis, which involves identifying an individualacross multiple cameras and views. However, it is a challenging task due tochanges in illumination, background, and viewpoint. Efficient featureextraction and metric learning algorithms are essential for a successful PRe-IDsystem. This paper proposes a novel approach for PRe-ID, which combines aConvolutional Neural Network (CNN) based feature extraction method withCross-view Quadratic Discriminant Analysis (XQDA) for metric learning.Additionally, a matching algorithm that employs Mahalanobis distance and ascore normalization process to address inconsistencies between camera scores isimplemented. The proposed approach is tested on four challenging datasets,including VIPeR, GRID, CUHK01, and PRID450S, and promising results areobtained. For example, without normalization, the rank-20 rate accuracies ofthe GRID, CUHK01, VIPeR and PRID450S datasets were 61.92%, 83.90%, 92.03%,96.22%; however, after score normalization, they have increased to 64.64%,89.30%, 92.78%, and 98.76%, respectively. Accordingly, the promising results onfour challenging datasets indicate the effectiveness of the proposed approach.

Improving CNN-based Person Re-identification using score Normalization