Article: Part-based pyramid loss for person re-identification Journal: International Journal of Information and Communication Technology (IJICT) 2019 Vol.15 No.4 pp.344 - 356 Abstract: Person re-identification (ReID) is a challenging problem in computer vision, meanwhile attracted the attention of industry. Person ReID focuses on identifying person among multiple different cameras. A key under-addressed problem is to learn a good metric for measuring the similarity among images. Recently, deep learning networks with metric learning loss has become a common framework for person ReID, such as triplet loss and its variants. However, the previous method mainly uses the distance to measure the similarity and the distance measure is more sensitive when the scale changes. In this paper, we propose part-based pyramid loss to learn better similarity metric for the person ReID, in which batches of quadruplet samples as the input. Specifically, we simultaneously use the relationship of distance and angle among samples learn the local body-parts features of person images. Our approach uses the pyramid relationship in triangles as a measure of similarity, minimising the angle at the negative point of the triangle. Pyramid loss can learn better similarity metric and achieve a higher performance on the person ReID benchmark datasets. The experimental results show that, our method yields competitive accuracy with the state-of-the-art methods. Inderscience Publishers - linking academia, business and industry through research

Title: Part-based pyramid loss for person re-identification

Authors: Yuanyuan Wang; Zhijian Wang; Mingxin Jiang

Addresses: College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China; College of Computer and Information, Hohai University, Nanjing, China ' College of Computer and Information, Hohai University, Nanjing, China ' College of Electronic Information Engineering, Huaiyin Institute of Technology, Huaian, China

Abstract: Person re-identification (ReID) is a challenging problem in computer vision, meanwhile attracted the attention of industry. Person ReID focuses on identifying person among multiple different cameras. A key under-addressed problem is to learn a good metric for measuring the similarity among images. Recently, deep learning networks with metric learning loss has become a common framework for person ReID, such as triplet loss and its variants. However, the previous method mainly uses the distance to measure the similarity and the distance measure is more sensitive when the scale changes. In this paper, we propose part-based pyramid loss to learn better similarity metric for the person ReID, in which batches of quadruplet samples as the input. Specifically, we simultaneously use the relationship of distance and angle among samples learn the local body-parts features of person images. Our approach uses the pyramid relationship in triangles as a measure of similarity, minimising the angle at the negative point of the triangle. Pyramid loss can learn better similarity metric and achieve a higher performance on the person ReID benchmark datasets. The experimental results show that, our method yields competitive accuracy with the state-of-the-art methods.

Keywords: person re-identification; ReiD; metric learning; pyramid loss; part-based.

DOI: 10.1504/IJICT.2019.103198

International Journal of Information and Communication Technology, 2019 Vol.15 No.4, pp.344 - 356

Received: 22 Sep 2018
Accepted: 22 Oct 2018
Published online: 22 Oct 2019 *

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Title: Part-based pyramid loss for person re-identification

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