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 |
|---|---|
| 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 |
|---|
Conference
| Conference | 2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015 |
|---|---|
| Country | United States |
| City | Waikoloa |
| Period | 15/1/5 → 15/1/9 |
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All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computer Vision and Pattern Recognition
Cite this
}
An ensemble color model for human re-identification. / Liu, Xiaokai; Wang, Hongyu; Wu, Yi; Yang, Jimei; Yang, Ming Hsuan.
Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 868-875 7045974 (Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - An ensemble color model for human re-identification
AU - Liu, Xiaokai
AU - Wang, Hongyu
AU - Wu, Yi
AU - Yang, Jimei
AU - Yang, Ming Hsuan
PY - 2015/2/19
Y1 - 2015/2/19
N2 - 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%.
AB - 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%.
UR - http://www.scopus.com/inward/record.url?scp=84925385115&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84925385115&partnerID=8YFLogxK
U2 - 10.1109/WACV.2015.120
DO - 10.1109/WACV.2015.120
M3 - Conference contribution
AN - SCOPUS:84925385115
T3 - Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015
SP - 868
EP - 875
BT - Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -