Human Body Multiple Parts Parsing for Person Reidentification Based on Xception

Authors

Sibo Qiao^1, , Shanchen Pang^{1, *,} , Xue Zhai^1, , Min Wang², Shihang Yu^3, , Tong Ding^4, , Xiaochun Cheng^{5, *}

¹College of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong, China

²College of Control Science and Engineering, China University of Petroleum, Qingdao, Shandong, China

³College of Mechanical Engineering, Tiangong University, Tianjin, China

⁴College of Software, Shandong University, Jinan, Shandong, China

⁵School of Science and Technology, Middlesex University, The Burroughs, Hendon, London

^*Corresponding author. Email: pangsc@upc.edu.cn and x.cheng@mdx.ac.uk

DOI

10.2991/ijcis.d.201222.001How to use a DOI?

Keywords

Person reidentification; Semantic parsing; Global representations; Local representations

Abstract

A mass of information grows explosively in socially networked industries, as extensive data, such as images and texts, is captured by vast sensors. Pedestrians are the main initiators of various activities in socially networked industries, hence, it is very important to quickly obtain relevant information of pedestrians from a large number of images. Person reidentification is an image retrieval technology, which can immediately retrieve target person in abundant images. However, due to the complexity of many important factors especially of changeful poses, occlusion and background clutter, person reidentification still faces extensive challenges. Considering these challenges, robust and distinguishing person representations are hard to be extracted well to identify different people. In this paper, to obtain more discriminative representations, we propose a human body multiple parts parsing (BMPP) architecture, which captures local pixel-level representations from body parts and global representations from whole body simultaneously. Additionally, a straightforward preprocessing method is adopted in this paper to improve the resolution of images in person reidentification benchmarks. To eliminate the negative effects of changeful poses, a simple yet effective representation fusion strategy is used for the original and horizontally flipped images to get final representations. Experimental results indicate that the method proposed in this article attains superior performance to most of state-of-the-art methods on CUHK03 and Market-1501.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Sibo Qiao
AU  - Shanchen Pang
AU  - Xue Zhai
AU  - Min Wang
AU  - Shihang Yu
AU  - Tong Ding
AU  - Xiaochun Cheng
PY  - 2020
DA  - 2020/12/29
TI  - Human Body Multiple Parts Parsing for Person Reidentification Based on Xception
JO  - International Journal of Computational Intelligence Systems
SP  - 482
EP  - 490
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.201222.001
DO  - 10.2991/ijcis.d.201222.001
ID  - Qiao2020
ER  -