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Resolution invariant person reid based on feature transformation and self-weighted attention

2021-01-12 15:22:41
Ziyue Zhang, Shuai Jiang, Congzhentao Huang, Richard Yi Da Xu

Abstract

tract: Person Re-identification (ReID) is a critical computer vision task that aims to match the same person in images or video sequences. Most current works focus on settings where resolution is kept the same. However, the resolution is a crucial factor in person ReID when the cameras are at different distances from the person or the camera's model are different from each other. In this paper, we propose a novel two-stream network with a lightweight resolution association ReID feature transformation (RAFT) module and a self-weighted attention (SWA) ReID module to evaluate features under different resolutions. RAFT transforms the low resolution features to corresponding high resolution features. SWA evaluates both features to get weight factors for the person ReID. Both modules are jointly trained to get a resolution invariant representation. Extensive experiments on five benchmark datasets show the effectiveness of our method. For instance, we achieve Rank-1 accuracy of 43.3% and 83.2% on CAVIAR and MLR-CUHK03, outperforming the state-of-the-art.

Abstract (translated)

URL

https://arxiv.org/abs/2101.04544

PDF

https://arxiv.org/pdf/2101.04544


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