Progressive Bilateral-Context Driven Model for Post-Processing Person Re-Identification

doi:10.1109/TMM.2020.2994524

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能制造技术与系统研究中心 > 多维数据分析团队

	Progressive Bilateral-Context Driven Model for Post-Processing Person Re-Identification
	Cao, Min 1,4,5; Chen, Chen 1; Dou, Hao 1; Hu, Xiyuan 2; Peng, Silong 1; Kuijper, Arjan 3,5
刊名	IEEE TRANSACTIONS ON MULTIMEDIA
	2021
卷号	23 页码:1239-1251
关键词	Probes Feature extraction Computational complexity Visualization Manifolds Context modeling Training Contextual information person re-identification post-processing
ISSN号	1520-9210
DOI	10.1109/TMM.2020.2994524
通讯作者	Chen, Chen(chen.chen@ia.ac.cn)
英文摘要	Most existing person re-identification methods compute pairwise similarity by extracting robust visual features and learning the discriminative metric. Owing to visual ambiguities, these content-based methods that determine the pairwise relationship only based on the similarity between them, inevitably produce a suboptimal ranking list. Instead, the pairwise similarity can be estimated more accurately along the geodesic path of the underlying data manifold by exploring the rich contextual information of the sample. In this paper, we propose a lightweight post-processing person re-identification method in which the pairwise measure is determined by the relationship between the sample and the counterpart's context in an unsupervised way. We translate the point-to-point comparison into the bilateral point-to-set comparison. The sample's context is composed of its neighbor samples with two different definition ways: the first order context and the second order context, which are used to compute the pairwise similarity in sequence, resulting in a progressive post-processing model. The experiments on four large-scale person re-identification benchmark datasets indicate that (1) the proposed method can consistently achieve higher accuracies by serving as a post-processing procedure after the content-based person re-identification methods, showing its state-of-the-art results, (2) the proposed lightweight method only needs about 6 milliseconds for optimizing the ranking results of one sample, showing its high-efficiency. Code is available at: https://github.com/123ci/PBCmodel.
资助项目	National Key R&D Program of China[25904] ; National Natural Science Foundation of China[NSFC 61906194]
WOS关键词	NETWORK
WOS研究方向	Computer Science ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000645068200006
资助机构	National Key R&D Program of China ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/44506]
专题	自动化研究所_智能制造技术与系统研究中心_多维数据分析团队
通讯作者	Chen, Chen
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China 3.Tech Univ Darmstadt, Math & Appl Visual Comp, D-64283 Darmstadt, Germany 4.Soochow Univ, Sch Comp Sci & Technol, Suzhou 215006, Peoples R China 5.Fraunhofer IGD, D-64283 Darmstadt, Germany
推荐引用方式 GB/T 7714	Cao, Min,Chen, Chen,Dou, Hao,et al. Progressive Bilateral-Context Driven Model for Post-Processing Person Re-Identification[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2021,23:1239-1251.
APA	Cao, Min,Chen, Chen,Dou, Hao,Hu, Xiyuan,Peng, Silong,&Kuijper, Arjan.(2021).Progressive Bilateral-Context Driven Model for Post-Processing Person Re-Identification.IEEE TRANSACTIONS ON MULTIMEDIA,23,1239-1251.
MLA	Cao, Min,et al."Progressive Bilateral-Context Driven Model for Post-Processing Person Re-Identification".IEEE TRANSACTIONS ON MULTIMEDIA 23(2021):1239-1251.