中文版 | English
Title

A part power set model for scale-free person retrieval

Author
Corresponding AuthorJi,Rongrong
DOI
Publication Years
2019
ISSN
1045-0823
Source Title
Volume
2019-August
Pages
3397-3403
Abstract
Recently, person re-identification (re-ID) has attracted increasing research attention, which has broad application prospects in video surveillance and beyond. To this end, most existing methods highly relied on well-aligned pedestrian images and hand-engineered part-based model on the coarsest feature map. In this paper, to lighten the restriction of such fixed and coarse input alignment, an end-to-end part power set model with multi-scale features is proposed, which captures the discriminative parts of pedestrians from global to local, and from coarse to fine, enabling part-based scale-free person re-ID. In particular, we first factorize the visual appearance by enumerating k-combinations for all k of n body parts to exploit rich global and partial information to learn discriminative feature maps. Then, a combination ranking module is introduced to guide the model training with all combinations of body parts, which alternates between ranking combinations and estimating an appearance model. To enable scale-free input, we further exploit the pyramid architecture of deep networks to construct multi-scale feature maps with a feasible amount of extra cost in term of memory and time. Extensive experiments on the mainstream evaluation datasets, including Market-1501, DukeMTMC-reID and CUHK03, validate that our method achieves the state-of-the-art performance.
SUSTech Authorship
Others
Language
English
URL[Source Record]
Funding Project
Key Technologies Research and Development Program[2016YFB1001503];Key Technologies Research and Development Program[2017J01125];Key Technologies Research and Development Program[2017M612134];Key Technologies Research and Development Program[2017YFC0113000];Key Technologies Research and Development Program[2018J01106];Key Technologies Research and Development Program[61572410];Key Technologies Research and Development Program[61772443];Key Technologies Research and Development Program[U1705262];
Scopus EID
2-s2.0-85074923274
Data Source
Scopus
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/382652
DepartmentSouthern University of Science and Technology
Affiliation
1.Fujian Key Laboratory of Sensing and Computing for Smart City,School of Information Science and Engineering,Xiamen University,361005,China
2.Peng Cheng Laborotory,China
3.Xi'an Jiaotong University,China
4.University of Oulu,Finland
5.Southern University of Science and Technology,China
6.Tencent Youtu Lab,Tencent Technology (Shanghai) Co.,Ltd,
Recommended Citation
GB/T 7714
Shen,Yunhang,Ji,Rongrong,Hong,Xiaopeng,et al. A part power set model for scale-free person retrieval[C],2019:3397-3403.
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