Abstract
The past decade has seen the rapid development of deep learning methods in person re-identification. However, most existing models ignore the sharing and exclusive information between different images, leading to insufficient discriminative information from inter-class images. Therefore, this paper proposes a multi-branch graph convolution model to learn the sharing and exclusive information both at the global and local levels. First, using multi-granularity features as graph nodes and combining cosine similarity to construct local and global graphs to mine the relationships between pedestrian images; then, embedding the inter-local and global relationships into the feature representation of pedestrian images by graph convolution operations; finally, combining identity loss and component segmentation loss as the final loss function for the model training. Experimental results on the Market-1501, CUHK03, and DukeMTMC-reID datasets show that the proposed method can effectively improve the performance of person re-identification.
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
References
Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). In: Proceedings of the European Conference on Computer Vision, pp. 480–496. Springer, Heidelberg (2018)
Fu, J., et al.: Dual attention network for scene segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 3146–3154. IEEE, Piscataway (2019)
Wang, G., Yuan, Y., Chen, X., Li, J., Zhou, X.: Learning discriminative features with multiple granularities for person re-identification. In: Proceedings of the 26th ACM international conference on Multimedia, pp. 274–282. ACM, New York (2018)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907
Ding, C., Wang, K., Wang, P., Tao, D.: Multi-task learning with coarse priors for robust part-aware person re-identification. IEEE Trans. Pattern Anal. Mach. Intell. 44(3), 1474–1488 (2020)
Luo, H., Jiang, W., Zhang, X., Fan, X., Qian, J., Zhang, C.: AligneDreid++: dynamically matching local information for person re-identification. Pattern Recogn. 94(1), 53–61 (2019)
Guo, J., Yuan, Y., Huang, L., Zhang, C., Yao, J.-G., Han, K.: Beyond human parts: dual part-aligned representations for person re-identification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3642–3651. IEEE, Piscataway (2019)
Zheng, F., et al.: Pyramidal person re-identification via multi-loss dynamic training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8514–8522. IEEE, Piscataway (2019)
Zhang, J., Li, X., Chen, C., Qi, M., Wu, J., Jiang, J.: Global-local graph convolutional network for cross-modality person re-identification. Neurocomputing 452(10), 137–146 (2021)
Yang, J., Zheng, W.-S., Yang, Q., Chen, Y.-C., Tian, Q.: Spatial-temporal graph convolutional network for video-based person re-identification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 3289–3299. IEEE, Piscataway (2020)
Nguyen, B.X., Nguyen, B.D., Do, T., Tjiputra, E., Tran, Q.D., Nguyen, A.: Graph-based person signature for person re-identifications. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3492–3501. IEEE, Piscataway (2021)
Jiang, B., Wang, X., Luo, B.: PH-GCN: Person re-identification with part-based hierarchical graph convolutional network. arXiv preprint arXiv:1907.08822
Zhang, Z., Zhang, H., Liu, S., Xie, Y., Durrani, T.S.: Part-guided graph convolution networks for person re-identification. Pattern Recogn. 120, 108155 (2021)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE, Piscataway (2016)
Güler, R.A., Neverova, N., Kokkinos, I.: Densepose: dense human pose estimation in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7297–7306. IEEE, Piscataway (2018)
Huang, H., et al.: Improve person re-identification with part awareness learning. IEEE Trans. Image Process. 29, 7468–7481 (2020)
Simonovsky, M., Komodakis, N.: Dynamic edge-conditioned filters in convolutional neural networks on graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3693–3702. IEEE, Piscataway (2017)
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: Proceedings of the IEEE international conference on computer vision, pp. 1116–1124. IEEE, Piscataway (2015)
Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3754–3762. IEEE, Piscataway (2017)
Li, W., Zhao, R., Xiao, T., Wang, X.: 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. IEEE, Piscataway (2014)
Lin, Y., et al.: Improving person re-identification by attribute and identity learning. Pattern Recogn. 95, 151–161 (2019)
Kalayeh, M.M., Basaran, E., Gökmen, M., Kamasak, M.E., Shah, M.: Human semantic parsing for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1062–1071. IEEE, Piscataway (2018)
Luo, C., Chen, Y., Wang, N., Zhang, Z.: Spectral feature transformation for person re-identification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4976–4985. IEEE, Piscataway (2019)
Zhu, K., Guo, H., Liu, Z., Tang, M., Wang, J.: Identity-guided human semantic parsing for person re-identification. In: European Conference on Computer Vision, pp. 346–363. Springer, Heidelberg (2020)
Chen, D., Xu, D., Li, H., Sebe, N., Wang, X.: Group consistent similarity learning via deep CRF for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8649–8658. IEEE, Piscataway (2018)
Shen, Y., Li, H., Yi, S., Chen, D., Wang, X.: Person re-identification with deep similarity-guided graph neural network. In: Proceedings of the European Conference on Computer Vision, pp. 486–504. Springer, Heidelberg (2018)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 62172139, in part by the Natural Science Foundation of Hebei Province under Grant F2020201025, F2019201151, F2019201362 and F2018210148, in part by the Science Research Project of Hebei Province under Grant BJ2020030 and QN2017306, in part by the Open Project Program of NLPR under Grant 202200007, in part by the Foundation of President of Hebei University under Grant XZJJ201909 and XZJJ201906. This work was also supported by the High-Performance Computing Center of Hebei University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhao, S., Cheng, H., Liu, S., Meng, L. (2023). Graph-Based Multi-granularity Person Re-identification. In: You, P., Li, H., Chen, Z. (eds) Proceedings of International Conference on Image, Vision and Intelligent Systems 2022 (ICIVIS 2022). ICIVIS 2022. Lecture Notes in Electrical Engineering, vol 1019. Springer, Singapore. https://doi.org/10.1007/978-981-99-0923-0_14
Download citation
DOI: https://doi.org/10.1007/978-981-99-0923-0_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-0922-3
Online ISBN: 978-981-99-0923-0
eBook Packages: EngineeringEngineering (R0)