IEICE TRANSACTIONS on Information
Person Re-Identification by Spatial Pyramid Color Representation and Local Region Matching
Summary :
Person re-identification is challenging due to illumination changes and viewpoint variations in the multi-camera environment. In this paper, we propose a novel spatial pyramid color representation (SPCR) and a local region matching scheme, to explore person appearance for re-identification. SPCR effectively integrates color layout into histogram, forming an informative global feature. Local region matching utilizes region statistics, which is described by covariance feature, to find appearance correspondence locally. Our approach shows robustness to illumination changes and slight viewpoint variations. Experiments on a public dataset demonstrate the performance superiority of our proposal over state-of-the-art methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E95-D No.8 pp.2154-2157
- Publication Date
- 2012/08/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E95.D.2154
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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Chunxiao LIU, Guijin WANG, Xinggang LIN, Liang LI, "Person Re-Identification by Spatial Pyramid Color Representation and Local Region Matching" in IEICE TRANSACTIONS on Information,
vol. E95-D, no. 8, pp. 2154-2157, August 2012, doi: 10.1587/transinf.E95.D.2154.
Abstract: Person re-identification is challenging due to illumination changes and viewpoint variations in the multi-camera environment. In this paper, we propose a novel spatial pyramid color representation (SPCR) and a local region matching scheme, to explore person appearance for re-identification. SPCR effectively integrates color layout into histogram, forming an informative global feature. Local region matching utilizes region statistics, which is described by covariance feature, to find appearance correspondence locally. Our approach shows robustness to illumination changes and slight viewpoint variations. Experiments on a public dataset demonstrate the performance superiority of our proposal over state-of-the-art methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.2154/_p
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@ARTICLE{e95-d_8_2154,
author={Chunxiao LIU, Guijin WANG, Xinggang LIN, Liang LI, },
journal={IEICE TRANSACTIONS on Information},
title={Person Re-Identification by Spatial Pyramid Color Representation and Local Region Matching},
year={2012},
volume={E95-D},
number={8},
pages={2154-2157},
abstract={Person re-identification is challenging due to illumination changes and viewpoint variations in the multi-camera environment. In this paper, we propose a novel spatial pyramid color representation (SPCR) and a local region matching scheme, to explore person appearance for re-identification. SPCR effectively integrates color layout into histogram, forming an informative global feature. Local region matching utilizes region statistics, which is described by covariance feature, to find appearance correspondence locally. Our approach shows robustness to illumination changes and slight viewpoint variations. Experiments on a public dataset demonstrate the performance superiority of our proposal over state-of-the-art methods.},
keywords={},
doi={10.1587/transinf.E95.D.2154},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Person Re-Identification by Spatial Pyramid Color Representation and Local Region Matching
T2 - IEICE TRANSACTIONS on Information
SP - 2154
EP - 2157
AU - Chunxiao LIU
AU - Guijin WANG
AU - Xinggang LIN
AU - Liang LI
PY - 2012
DO - 10.1587/transinf.E95.D.2154
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2012
AB - Person re-identification is challenging due to illumination changes and viewpoint variations in the multi-camera environment. In this paper, we propose a novel spatial pyramid color representation (SPCR) and a local region matching scheme, to explore person appearance for re-identification. SPCR effectively integrates color layout into histogram, forming an informative global feature. Local region matching utilizes region statistics, which is described by covariance feature, to find appearance correspondence locally. Our approach shows robustness to illumination changes and slight viewpoint variations. Experiments on a public dataset demonstrate the performance superiority of our proposal over state-of-the-art methods.
ER -
English
