University of Oulu

H. Li, M. Yang, Z. Lai, W. Zheng and Z. Yu, "Pedestrian re-Identification Based on Tree Branch Network with Local and Global Learning," 2019 IEEE International Conference on Multimedia and Expo (ICME), Shanghai, China, 2019, pp. 694-699. doi: 10.1109/ICME.2019.00125

Pedestrian re-identification based on tree branch network with local and global learning

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Author: Li, Hui1; Yang, Meng2; Lai, Zhihui1;
Organizations: 1Shenzhen University
2Sun Yat-sen University
3University of Oulu
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.7 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-02-19


Deep part-based methods in recent literature have revealed the great potential of learning local part-level representation for pedestrian image in the task of person re-identification. However, global features that capture discriminative holistic information of human body are usually ignored or not well exploited. This motivates us to investigate joint learning global and local features from pedestrian images. Specifically, in this work, we propose a novel framework termed tree branch network (TBN) for person re-identification. Given a pedestrain image, the feature maps generated by the backbone CNN, are partitioned recursively into several pieces, each of which is followed by a bottleneck structure that learns finer-grained features for each level in the hierarchical tree-like framework. In this way, representations are learned in a coarse-to-fine manner and finally assembled to produce more discriminative image descriptions. Experimental results demonstrate the effectiveness of the global and local feature learning method in the proposed TBN framework. We also show significant improvement in performance over state-of-the-art methods on three public benchmarks: Market-1501, CUHK-03 and DukeMTMC.

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Series: IEEE International Conference on Multimedia and Expo
ISSN: 1945-7871
ISSN-E: 1945-788X
ISSN-L: 1945-7871
ISBN: 978-1-5386-9552-4
ISBN Print: 978-1-5386-9553-1
Pages: 694 - 699
Article number: 8784751
DOI: 10.1109/ICME.2019.00125
Host publication: 2019 IEEE International Conference on Multimedia and Expo, ICME 2019, 8-12 July 2019 Shanghai, China
Conference: IEEE International Conference on Multimedia and Expo
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
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