An ensemble color model for human re-identification

Xiaokai Liu; Hongyu Wang; Yi Wu; Jimei Yang; Ming Hsuan Yang

doi:10.1109/WACV.2015.120

An ensemble color model for human re-identification

Xiaokai Liu, Hongyu Wang, Yi Wu, Jimei Yang, Ming Hsuan Yang

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

43 Citations (Scopus)

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	https://doi.org/10.1109/WACV.2015.120
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

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

All Science Journal Classification (ASJC) codes

Computer Science Applications
Computer Vision and Pattern Recognition

Access to Document

10.1109/WACV.2015.120

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Liu, X., Wang, H., Wu, Y., Yang, J., & Yang, M. H. (2015). An ensemble color model for human re-identification. In Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015 (pp. 868-875). [7045974] (Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WACV.2015.120

Liu, Xiaokai ; Wang, Hongyu ; Wu, Yi ; Yang, Jimei ; Yang, Ming Hsuan. / An ensemble color model for human re-identification. Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 868-875 (Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015).

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title = "An ensemble color model for human re-identification",

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%.",

author = "Xiaokai Liu and Hongyu Wang and Yi Wu and Jimei Yang and Yang, {Ming Hsuan}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015 ; Conference date: 05-01-2015 Through 09-01-2015",

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doi = "10.1109/WACV.2015.120",

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Liu, X, Wang, H, Wu, Y, Yang, J & Yang, MH 2015, An ensemble color model for human re-identification. in Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015., 7045974, Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, Institute of Electrical and Electronics Engineers Inc., pp. 868-875, 2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015, Waikoloa, United States, 15/1/5. https://doi.org/10.1109/WACV.2015.120

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

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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%.

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Liu X, Wang H, Wu Y, Yang J, Yang MH. An ensemble color model for human re-identification. In 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). https://doi.org/10.1109/WACV.2015.120

An ensemble color model for human re-identification

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