Enhancing person re-identification with distance fusion: A comparative study on multiple datasets

Abstract

Person re-identification (Re-ID) is a critical task in video surveillance and security systems, aiming to match individuals across non-overlapping camera views. However, significant challenges arise due to variations in illumination, pose, and viewpoint. In this work, we propose a distance fusion-based approach to improve person Re-ID accuracy by effectively combining multiple feature-based distance metrics. We extract discriminative features using Local Maximal Occurrence (LOMO), Gaussian of Gaussian (GOG), and Convolutional Neural Networks (CNN). The extracted features are projected into a lower-dimensional space using Cross-view Quadratic Discriminant Analysis (XQDA), and their distances are computed and normalized using Min-Max scaling. To enhance performance, we implement two fusion strategies: Distance Fusion by Simple Sum (DFSS), an unsupervised method that aggregates normalized distances, and Distance Fusion by Logistic Regression (DFLR), a supervised approach that learns an optimal combination of distances. The proposed techniques are evaluated on four benchmark datasets: VIPeR, PRID450S, GRID, and CUHK01. Experimental results demonstrate that our fusion-based methods significantly outperform individual feature-based approaches, achieving state-of-the-art accuracy. Specifically, the fusion of CNN, LOMO, and GOG in the DFLR framework achieves the best performance across all datasets, with improvements in Rank-1 and Rank-20 accuracy. These findings highlight the effectiveness of distance fusion in improving person Re-ID performance, making it a promising approach for real-world applications.