Abstract
Unsupervised multi-source domain adaptation facilitates the transfer of knowledge from multiple source domains, which possess labeled data, to an unlabeled target domain. Pedestrian re-identification is a technique for cross-camera pedestrian retrieval in surveillance data. The utilization of multiple source domains holds significant research implications, particularly in scenarios involving a substantial volume of data. Recently, efforts have been made to eliminate distributional differences between data from different domains. However, these approaches do not take into account the specificity of the target domain. Furthermore, when using graph convolutional networks for domain fusion, few studies have explored the utilization of deep correlations between nodes, which is crucial for node updates in GCNs. In this paper, we present a novel methodology that enhances domain fusion and showcases robust performance. In particular, we first propose a Domain Fusion Module based on the prioritization of target domains, which enables the fusion of domain information. Second, in order to mine the deep correlation between nodes, we process it by introducing multidimensional edge features. Our multiple experimental results using four datasets on three migration tasks demonstrate the superior performance of the DFTM, thus providing strong support for the effectiveness of our approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sun, Y., Zheng, L., Yang, Y., Tian, Qi., Wang, S.: Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part IV, pp. 501–518. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_30
Wang, G., Yuan, Y., Chen, X., Li, J., Zhou, X.: Learning discriminative features with multiple granularities for person re identification. In: 2018 ACM Multimedia Conference on Multimedia Conference, MM 2018, Seoul, Republic of Korea, 22–26 October 2018, pp. 274–282 (2018)
Zhang, Z., Lan, C., Zeng, W., Jin, X., Chen, Z.: Relation-aware global attention for person reidentification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Song, L., et al.: Unsupervised domain adaptive re-identification: theory and practice. Pattern Recogn. 102, 107173 (2020)
Yang, F., et al.: Asymmetric co-teaching for unsupervised cross-domain person re-identification. In: AAAI, pp. 12597–12604 (2020)
Ge, Y., Chen, D., Li, H.: Mutual meanteaching: pseudo label refinery for unsupervised domain adaptation on person re-identification. In: International Conference on Learning Representations (2020)
Bai, Z., Wang, Z., Wang, J., et al.: Unsupervised multi-source domain adaptation for person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12914–12923 (2021)
Yuan, J., et al.: Self-supervised graph neural network for multi-source domain adaptation (2022)
Deng, W., Zheng, L., Ye, Q., et al.: Similarity-preserving image-image domain adaptation for person re-identification. arXiv preprint arXiv:1811.10551 (2018)
Wei, L., Zhang, S., Gao, W., et al.: Person transfer gan to bridge domain gap for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 79–88 (2018)
Zhai, Y., Lu, S., Ye, Q., et al.: Ad-cluster: augmented discriminative clustering for domain adaptive person re-identification. In: Proceedings of the IEEE/ CVF Conference on Computer Vision and Pattern Recognition, pp. 9021–9030 (2020)
Mekhazni, D., Bhuiyan, A., Ekladious, G., Granger, E.: Unsupervised domain adaptation in the dissimilarity space for person re-identification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12372, pp. 159–174. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58583-9_10
Huang, Y., Peng, P., Jin, Y., et al.: Domain adaptive attention learning for unsupervised person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 07, pp. 11069–11076 (2020)
Yang, Q., Yu, H.X., Wu, A., et al.: Patch-based discriminative feature learning for unsupervised person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3633–3642 (2019)
Tao, X., Kong, J., Jiang, M., et al.: Unsupervised domain adaptation by multi-loss gap minimization learning for person re-identification. IEEE Trans. Circuits Syst. Video Technol. 32(7), 4404–4416 (2021)
Qi, L., Liu, J., Wang, L., et al.: Unsupervised generalizable multi-source person re-identification: a domain-specific adaptive framework. Pattern Recogn. 140, 109546 (2023)
Wang, H., Hu, J., Zhang, G.: Multi-source transfer network for cross domain person re-identification. IEEE Access 8, 83265–83275 (2020)
Zheng, L., Shen, L., Tian, L., et al.: Scalable person re-identification: a benchmark. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1116–1124 (2015)
Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 17–35. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_2
Li, W., Zhao, R., Xiao, T., et al.: Deepreid: deep filter pairing neural network for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 152–159 (2014)
Xiang, S., Fu, Y., You, G., et al.: Unsupervised domain adaptation through synthesis for person re-identification. In: 2020 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2020)
Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: past, present and future. arXiv preprint arXiv:1610.02984 (2016)
Ye, M., Shen, J., Lin, G., et al.: Deep learning for person re-identification: a survey and outlook. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 2872–2893 (2021)
Bruna, J., Zaremba, W., Szlam, A., et al. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. Adv. Neural Inf. Process. Syst. 29 (2016)
Atwood, J., Towsley, D.: Diffusion-convolutional neural networks. Adv. Neural Inf. Process. Syst. 29 (2016)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. Adv. Neural Inf. Process. Syst. 30 (2017)
Velickovic, P., Cucurull, G., Casanova, A., et al.: Graph attention networks. Stat 1050(20), 10.48550 (2017)
Xu, K., Hu, W., Leskovec, J., et al.: How powerful are graph neural networks?. arXiv preprint arXiv:1810.00826 (2018)
Gong, L., Cheng, Q.: Exploiting edge features for graph neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9211–9219 (2019)
Acknowledgments
This research received partial support from the R&D plan project in the key scientific research platform of universities in Guangdong Province (No. 2022KSYS016), the Guangdong Province Key Laboratory of Intelligent Detection in Complex Environment of Aerospace, Land and Sea (2022KSYS016), and the Research Platforms and Projects in Ordinary Universities in Guangdong Province (Natural Science) 2019KTSCX216.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
He, J. et al. (2024). Unsupervised Multi-source Adaptive Pedestrian Re-recognition: Based on Target Domain Prioritization and Multi-dimensional Edge Features. In: Leung, V.C., Li, H., Hu, X., Ning, Z. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Systems. Qshine 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 574. Springer, Cham. https://doi.org/10.1007/978-3-031-65123-6_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-65123-6_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-65122-9
Online ISBN: 978-3-031-65123-6
eBook Packages: Computer ScienceComputer Science (R0)