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A novel unsupervised person re-identification algorithm based on soft multi-label and compound attention model

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Abstract

To explore discriminative information fully and keep consistence of labels, an unsupervised person re-identification algorithm based on soft multi-label and compound attention model was proposed in this study. Based on learning of reference agent labels, soft multi-label was built by constructing a mapping model of targets and reference datasets. Later, soft multi-label was added into initial samples through deep convolutional network training to realize accurate labeling of targets and fine-grain classification of features under multi-camera scenes. In the training stage of the deep network, a compound attention mechanism is added between the convolution blocks to fuse the complementary information of the multiple channels features and the spaces domain features, therefore the potential discriminative information is explored. In addition, a weight fusion of distance loss function, label consistency loss function, and reference agent loss function was performed to distinguish hard negative pair set and realize matching of multi-camera labels. Since learning rate is the key influencing factor against the improvement of identification precision and training speed, a rectified adaptive moment estimation was adopted to achieve adaptive control of learning rate, accelerate training convergence of network and increase the robustness of the proposed algorithm. The proposed algorithm is proved by an experiment that it can increase identification precision significantly. The rank-1 of the proposed algorithm is at least 3.9% higher, and its mean average precision (mAP) is at least 4.7% higher compared to those of similar representative algorithms.

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Acknowledgments

The authors thank the anonymous reviewers and editors for the very constructive comments. This work was supported by the National Natural Science Foundation of China(61962046, 62001255, 61841204). Inner Mongolia Outstanding Youth Cultivation Fund(2018JQ02). Inner Mongolia Science and Technology Plan Project (Research and implementation of key technologies for intelligent analysis platform of traffic big data). Inner Mongolia Science and Technology Plan Project. Inner Mongolia Natural Science Foundation (2019MS06003).

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Authors and Affiliations

  1. School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China

    Zhang Baohua, Zhu Siyu, Zhou Yufeng, Gu Yu, Li Jianjun & Liu Xin

  2. Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, Baotou, 014010, Inner Mongolia, China

    Zhang Baohua, Lu Xiaoqi, Gu Yu, Li Jianjun & Liu Xin

  3. School of Information Engineering, Inner Mongolia Industrial University, Huhehaote, 010051, Inner Mongolia, China

    Lu Xiaoqi

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  1. Zhang Baohua
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  2. Zhu Siyu
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  3. Zhou Yufeng
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  4. Lu Xiaoqi
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  5. Gu Yu
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  6. Li Jianjun
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  7. Liu Xin
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Correspondence to Zhang Baohua.

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Baohua, Z., Siyu, Z., Yufeng, Z. et al. A novel unsupervised person re-identification algorithm based on soft multi-label and compound attention model. Multimed Tools Appl 81, 24081–24098 (2022). https://doi.org/10.1007/s11042-022-12728-z

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  • DOI: https://doi.org/10.1007/s11042-022-12728-z

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