Resolution based Feature Distillation for Cross Resolution Person Re-Identification
This addresses a practical issue in surveillance and security for person re-identification, but it is incremental as it builds on existing methods by handling multiple resolutions more effectively.
The paper tackles the problem of person re-identification across varying image resolutions, which degrades performance in real-world scenarios, and proposes a Resolution based Feature Distillation approach that learns resolution-invariant features, improving performance on multi-resolution datasets while maintaining comparable results in single-resolution cases.
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).