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Learning discriminative features for semi-supervised person re-identification

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Abstract

We focus on the one-example person re-identification (Re-ID) task, where each identity has only one labeled example along with many unlabeled examples. Since each identity has only one labeled example, the number of initialized label examples is small, and the body parts of person are not aligned due to changes in person pose and camera angle under the camera. Therefore, the distinguishing information of learning labeled and unlabeled examples is challenging. To overcome these problems, we propose an end-to-end multi-task training network for semi-supervised Re-ID. First, we impose a part segmentation (PS) constraint on feature maps, forcing a module to predict part labels from the feature maps and enhance alignment. Second, we carefully design the network named Multiple Branch Network (MBN). MBN is a multi-branch deep network architecture, which consisting of one branch for global feature representation and two branches for local feature representation, local feature representation that including horizontal stripes representation and PS representation, respectively. Finally, loss function fusion is designed to learn discriminative features for semi-supervised Re-ID. Specifically, the MBN model is optimized by mining the object classification loss, exclusive loss and PS loss simultaneously. We validate the effectiveness of our approach by demonstrating its superiority over the state-of-the-art methods on the standard benchmark datasets, including Market-1501, DukeMTMC-reID. Notably, the rank-1 accuracy of our method outperforms the state-of-the-art method by 15.9 points (absolute, i.e., 71.7% vs. 55.8%) on Market-1501 and 8.9 points on DukeMTMC-reID.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61872326, No.61672475); Shandong Provincial Natural Science Foundation (ZR2019MF044).

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Authors and Affiliations

  1. College of Information Science and Engineering, Ocean University of China, Qingdao, 266000, China

    Huanhuan Cai, Lei Huang, Wenfeng Zhang & Zhiqiang Wei

  2. Jining Polytechnic, Jining, 272000, China

    Huanhuan Cai

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  1. Huanhuan Cai
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  2. Lei Huang
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Correspondence to Lei Huang.

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Cai, H., Huang, L., Zhang, W. et al. Learning discriminative features for semi-supervised person re-identification. Multimed Tools Appl 81, 1787–1809 (2022). https://doi.org/10.1007/s11042-021-11420-y

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