Recover and identify: A generative dual model for cross-resolution person re-identification

Yu Jhe Li, Yun Chun Chen, Yen Yu Lin, Xiaofei Du, Yu Chiang Frank Wang

研究成果: Conference contribution同行評審

14 引文 斯高帕斯(Scopus)

摘要

Person re-identification (re-ID) aims at matching images of the same identity across camera views. Due to varying distances between cameras and persons of interest, resolution mismatch can be expected, which would degrade person re-ID performance 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 the missing details in low-resolution input images. The resulting features can be jointly applied for improving person re-ID performance due to preserving resolution invariance and recovering re-ID oriented discriminative details. Our experiments on five benchmark datasets confirm the effectiveness of our approach and its superiority over the state-of-the-art methods, especially when the input resolutions are unseen during training.

原文English
主出版物標題Proceedings - 2019 International Conference on Computer Vision, ICCV 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面8089-8098
頁數10
ISBN(電子)9781728148038
DOIs
出版狀態Published - 十月 2019
事件17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 - Seoul, Korea, Republic of
持續時間: 27 十月 20192 十一月 2019

出版系列

名字Proceedings of the IEEE International Conference on Computer Vision
2019-October
ISSN(列印)1550-5499

Conference

Conference17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
國家/地區Korea, Republic of
城市Seoul
期間27/10/192/11/19

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