Abstract
Appearance-based human re-identification is challenging due to different camera characteristics, varying lighting conditions, pose variations across camera views, etc. Recent studies have revealed that color information plays a critical role on performance. However, two problems remain unclear: (1) how do different color descriptors perform under the same scene in re-identification problem? and (2) how can we combine these descriptors without losing their invariance property and distinctiveness power? In this paper, we propose a novel ensemble model that combines different color descriptors in the decision level through metric learning. Experiments show that the proposed system significantly outperforms state-of-the-art algorithms on two challenging datasets (VIPeR and PRID 450S). We have improved the Rank 1 recognition rate on VIPeR dataset by 8.7%.
Original language | English |
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Title of host publication | Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 868-875 |
Number of pages | 8 |
ISBN (Electronic) | 9781479966820 |
DOIs | |
Publication status | Published - 2015 Feb 19 |
Event | 2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015 - Waikoloa, United States Duration: 2015 Jan 5 → 2015 Jan 9 |
Publication series
Name | Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015 |
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Conference
Conference | 2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015 |
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Country/Territory | United States |
City | Waikoloa |
Period | 15/1/5 → 15/1/9 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computer Vision and Pattern Recognition