Domain-aware unsupervised cross-dataset person re-identification

Zhihui Li, Wenhe Liu, Xiaojun Chang, Lina Yao, Mahesh Prakash, Huaxiang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

We focus on the person re-identification (re-id) problem of matching people across non-overlapping camera views. While most existing works rely on the abundance of labeled exemplars, we consider a more difficult unsupervised scenario, where no labeled exemplar is provided. One solution for unsupervised re-id that attracts much attention in the recent researches is cross-dataset transfer learning. It utilizes knowledge from multiple source datasets from different domains to enhance the unsupervised learning performance on the target domain. In previous works, much effect is taken on extraction of the generic and robust common appearances representations across domains. However, we observe that there also particular appearances in different domains. Simply ignoring these domain-unique appearances will misleading the matching schema in re-id application. Few unsupervised cross-dataset algorithms are proposed to learn the common appearances across multiple domains, even less of them consider the domain-unique representations. In this paper, we propose a novel domain-aware representation learning algorithm for unsupervised cross-dataset person re-id problem. The proposed algorithm not only learns a common appearances across-datasets but also captures the domain-unique appearances on the target dataset via minimization of the overlapped signal supports across different domains. Extensive experimental studies on benchmark datasets show superior performances of our algorithm over state-of-the-art algorithms. Sample analysis on selected samples also verifies the ability of diversity learning of our algorithm.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications
Subtitle of host publication15th International Conference, ADMA 2019 Dalian, China, November 21–23, 2019 Proceedings
EditorsJianxin Li, Sen Wang, Shaowen Qin, Xue Li, Shuliang Wang
Place of PublicationCham Switzerland
PublisherSpringer
Pages406-420
Number of pages15
ISBN (Electronic)9783030352318
ISBN (Print)9783030352301
DOIs
Publication statusPublished - 2019
EventInternational Conference on Advanced Data Mining and Applications 2019 - Dalian, China
Duration: 21 Nov 201923 Nov 2019
Conference number: 15th
http://adma2019.neusoft.edu.cn/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11888
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Advanced Data Mining and Applications 2019
Abbreviated titleADMA 2019
CountryChina
CityDalian
Period21/11/1923/11/19
Internet address

Keywords

  • Cross-dataset Re-ID
  • Domain-aware
  • Unsupervised person re-identification

Cite this

Li, Z., Liu, W., Chang, X., Yao, L., Prakash, M., & Zhang, H. (2019). Domain-aware unsupervised cross-dataset person re-identification. In J. Li, S. Wang, S. Qin, X. Li, & S. Wang (Eds.), Advanced Data Mining and Applications: 15th International Conference, ADMA 2019 Dalian, China, November 21–23, 2019 Proceedings (pp. 406-420). (Lecture Notes in Computer Science ; Vol. 11888 ). Springer. https://doi.org/10.1007/978-3-030-35231-8_29