Looking Alike From Far to Near: Enhancing Cross-Resolution Re-Identification via Feature Vector Panning
This addresses a domain-specific problem in surveillance for improving pedestrian re-identification across varying camera distances, offering a novel approach that is more efficient than existing methods.
The paper tackles the problem of cross-resolution re-identification (CR-ReID) in surveillance, where matching low-resolution images with high-resolution ones is challenging, and proposes a Vector Panning Feature Alignment (VPFA) framework that significantly outperforms previous state-of-the-art models with higher efficiency.
In surveillance scenarios, varying camera distances cause significant differences among pedestrian image resolutions, making it hard to match low-resolution (LR) images with high-resolution (HR) counterparts, limiting the performance of Re-Identification (ReID) tasks. Most existing Cross-Resolution ReID (CR-ReID) methods rely on super-resolution (SR) or joint learning for feature compensation, which increases training and inference complexity and has reached a performance bottleneck in recent studies. Inspired by semantic directions in the word embedding space, we empirically discover that semantic directions implying resolution differences also emerge in the feature space of ReID, and we substantiate this finding from a statistical perspective using Canonical Correlation Analysis and Pearson Correlation Analysis. Based on this interesting finding, we propose a lightweight and effective Vector Panning Feature Alignment (VPFA) framework, which conducts CR-ReID from a novel perspective of modeling the resolution-specific feature discrepancy. Extensive experimental results on multiple CR-ReID benchmarks show that our method significantly outperforms previous state-of-the-art baseline models while obtaining higher efficiency, demonstrating the effectiveness and superiority of our model based on the new finding in this paper.