Improving CNN-based Person Re-identification using score Normalization

TL;DR

This paper introduces a CNN-based person re-identification method enhanced with score normalization to improve matching accuracy across different camera views, demonstrating significant performance gains on multiple datasets.

Contribution

It combines CNN feature extraction with XQDA metric learning and a novel score normalization technique to address camera score inconsistencies in person re-identification.

Findings

Score normalization improved rank-20 accuracy on all datasets.

Achieved up to 98.76% accuracy on PRID450S dataset.

Demonstrated effectiveness across four challenging datasets.

Abstract

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, and promising results are obtained. For example, without normalization, the rank-20 rate accuracies of the 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 on four challenging datasets indicate the effectiveness of the proposed approach.