Resolution based Feature Distillation for Cross Resolution Person Re-Identification

TL;DR

This paper proposes a resolution-based feature distillation method that enhances cross-resolution person re-identification by learning resolution-invariant features, effectively handling multiple image resolutions and real-world degradations.

Contribution

It introduces a novel resolution-based feature distillation approach that filters resolution-related features, improving re-id performance across varying image resolutions.

Findings

Improves re-id accuracy with multiple resolutions

Achieves comparable results in single resolution scenarios

Effective on synthetic and real degraded datasets

Abstract

Person re-identification (re-id) aims to retrieve images of same identities across different camera views. Resolution mismatch occurs due to varying distances between person of interest and cameras, this significantly degrades the performance of re-id in real world scenarios. Most of the existing approaches resolve the re-id task as low resolution problem in which a low resolution query image is searched in a high resolution images gallery. Several approaches apply image super resolution techniques to produce high resolution images but ignore the multiple resolutions of gallery images which is a better realistic scenario. In this paper, we introduce channel correlations to improve the learning of features from the degraded data. In addition, to overcome the problem of multiple resolutions we propose a Resolution based Feature Distillation (RFD) approach. Such an approach learns resolution invariant features by filtering the resolution related features from the final feature vectors that are used to compute the distance matrix. We tested the proposed approach on two synthetically created datasets and on one original multi resolution dataset with real degradation. Our approach improves the performance when multiple resolutions occur in the gallery and have comparable results in case of single resolution (low resolution re-id).