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Open AccessArticle

Multi-Level Joint Feature Learning for Person Re-Identification

by Shaojun Wu 1,2 and Ling Gao 1,2,*
1
School of Information Science and Engineering, Shandong Normal University, Jinan 250014, Shandong Province, China
2
Institute of Data Science and Technology, Shandong Normal University, Jinan 250014, Shandong Province, China
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(5), 111; https://doi.org/10.3390/a13050111
Received: 1 April 2020 / Revised: 22 April 2020 / Accepted: 27 April 2020 / Published: 29 April 2020
(This article belongs to the Special Issue Algorithms for Smart Cities)
In person re-identification, extracting image features is an important step when retrieving pedestrian images. Most of the current methods only extract global features or local features of pedestrian images. Some inconspicuous details are easily ignored when learning image features, which is not efficient or robust to for scenarios with large differences. In this paper, we propose a Multi-level Feature Fusion model that combines both global features and local features of images through deep learning networks to generate more discriminative pedestrian descriptors. Specifically, we extract local features from different depths of network by the Part-based Multi-level Net to fuse low-to-high level local features of pedestrian images. Global-Local Branches are used to extract the local features and global features at the highest level. The experiments have proved that our deep learning model based on multi-level feature fusion works well in person re-identification. The overall results outperform the state of the art with considerable margins on three widely-used datasets. For instance, we achieve 96% Rank-1 accuracy on the Market-1501 dataset and 76.1% mAP on the DukeMTMC-reID dataset, outperforming the existing works by a large margin (more than 6%). View Full-Text
Keywords: deep learning; intelligent monitoring; person re-identification deep learning; intelligent monitoring; person re-identification
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Wu, S.; Gao, L. Multi-Level Joint Feature Learning for Person Re-Identification. Algorithms 2020, 13, 111.

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