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%.
|Title of host publication||Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|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
|Name||Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015|
|Conference||2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015|
|Period||15/1/5 → 15/1/9|
Bibliographical notePublisher Copyright:
© 2015 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computer Vision and Pattern Recognition