| Summary: | 碩士 === 國立臺灣大學 === 電信工程學研究所 === 107 === Person re-identification (re-ID) aims at matching images of the same identity across camera views. Due to varying distances between the cameras and persons of interest, resolution mismatch can be expected, which would degrade person re-ID performances in real-world scenarios. To overcome this problem, we propose a novel generative adversarial network to address cross-resolution person re-ID, allowing query images with varying resolutions. By advancing adversarial learning techniques, our proposed model learns resolution-invariant image representations while being able to recover missing details in low-resolution input images. Thus, the resulting features can be jointly applied for improved re-ID performances. Our experiments on three benchmark datasets confirm the effectiveness of our method and its superiority over the state-of-the-art approaches, especially when the input resolutions are unseen during training.
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