CASIA OpenIR  > 模式识别国家重点实验室  > 生物识别与安全技术
Perceptual hash-based feature description for person re-identification
Fang, Wen1; Hu, Hai-Miao1,2; Hu, Zihao1; Liao, Shengcai3; Li, Bo1,2
Source PublicationNEUROCOMPUTING
AbstractPerson re-identification is one of the most important and challenging problems in video surveillance systems. For person re-identification, feature description is a fundamental problem. While many approaches focus on exploiting low-level features to describe person images, most of them are not robust enough to illumination and viewpoint changes. In this paper, we propose a simple yet effective feature description method for person re-identification. Starting from low-level features, the proposed method uses perceptual hashing to binarize low-level feature maps and combines several feature channels for feature encoding. Then, an image pyramid is built, and three regional statistics are computed for hierarchical feature description. To some extent, the perceptual hash algorithm (PHA) can encode invariant macro structures of person images to make the representation robust to both illumination and viewpoint changes. On the other hand, while a rough hashing may be not discriminative enough, the combination of several different feature channels and regional statistics is able to exploit complementary information and enhance the discriminability. The proposed approach is evaluated on seven major person re-identification datasets. The results of comprehensive experiments show the effectiveness of the proposed method and notable improvements over the state-of-the-art approaches. (C) 2017 Elsevier B.V. All rights reserved.
KeywordPerson Re-identification Image Pyramid Regional Statistics Hierarchical Feature Description The Perceptual Hash Algorithm (Pha)
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Key Research and Development Program of China(2016YFC0801003) ; National Natural Science Foundation of China(61370121)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000413821400054
Citation statistics
Cited Times:24[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Beihang Univ, Beijing Key Lab Digital Media, Sch Comp Sci & Engn, Beijing 100191, Peoples R China
2.Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Fang, Wen,Hu, Hai-Miao,Hu, Zihao,et al. Perceptual hash-based feature description for person re-identification[J]. NEUROCOMPUTING,2018,272(1):520-531.
APA Fang, Wen,Hu, Hai-Miao,Hu, Zihao,Liao, Shengcai,&Li, Bo.(2018).Perceptual hash-based feature description for person re-identification.NEUROCOMPUTING,272(1),520-531.
MLA Fang, Wen,et al."Perceptual hash-based feature description for person re-identification".NEUROCOMPUTING 272.1(2018):520-531.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Fang, Wen]'s Articles
[Hu, Hai-Miao]'s Articles
[Hu, Zihao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Fang, Wen]'s Articles
[Hu, Hai-Miao]'s Articles
[Hu, Zihao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Fang, Wen]'s Articles
[Hu, Hai-Miao]'s Articles
[Hu, Zihao]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.