A deep model with combined losses for person re-identification. (May 2019)

• Record Type: Journal Article
• Authors: Wu, Di; Zheng, Si-Jia; Yuan, Chang-An; Huang, De-Shuang
• Is Part Of: Cognitive systems research. Volume 54(2019)
• Page Start: 74
• Page End: 82
• Publication Date: 2019-05
• Subjects: Convolutional Neural Networks -- Person Re-identification; Cognition -- Periodicals; Cognitive engineering (System design) -- Periodicals; Artificial intelligence -- Periodicals; 153.05
• DOI: 10.1016/j.cogsys.2018.04.003
• Languages: English
• ISSNs: 1389-0417

Abstract

Abstract: Person re-identification (PReID), which aims to re-identity a pedestrian from multiple non-overlapping cameras, has been significantly improved by deep learning system. There exist two popular deep frameworks used for PReID, i.e., identification and triplet models. Since these two frameworks have different loss functions, they have their own advantages and disadvantages. To combine the both advantages of two frameworks, in this paper, we propose using the triplet and Online Instance Matching (OIM) losses to train the carefully designed network. Given a triplet input images, the combined model can output the identities of the input images and learn a corresponding similarity measurement simultaneously. Experiments on CUHK01, CUHK03, Market-1501, and DukeMTMC-reID datasets demonstrate that the proposed model outperforms the compared state-of-the-art methods in most cases.