Abstract
Group re-identification (re-id) aims to retrieve a group of people across different surveillance cameras. Due to the change of group layout and membership, group re-id is much more difficult than person re-id. How to address these problems for group re-id is under-explored. In this paper, we propose the Adaptive Progressive group representation Learning Network (APLN) which consists of three innovations: First, we propose a progressive group representation method which fuses individual features together with relation features. Second, we propose a member mask to ignore the impact of changes in the number of members. The member mask is beneficial to getting a more robust group representation from volatile group samples. Third, we propose to use group proxy node as the global representation of the group context graph to obtain precise group context graph information by focusing on more significant individuals. Experimental results demonstrate that our proposed method outperforms the state-of-the-art performance on several group re-id datasets. Compared with the previous methods, the parameters of our model are much fewer and the inference speed is faster.
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
Zheng, W.S., Gong, S., Xiang, T.: Associating groups of people. In: British Machine Vision Conference (2009)
Zhu, J., Yang, H., Lin, W., Liu, N., Wang, J., Zhang, W.: Group re-identification with group context graph neural networks. IEEE Trans. Multimed. 23, 2614–2626 (2020)
Yan, Y., et al.: Learning multi-attention context graph for group-based re-identification. IEEE Trans. Pattern Anal. Mach. Intell. (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Wang, G., Lai, J., Huang, P., Xie, X.: Spatial-temporal person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8933–8940 (2019)
Zhang, Q., Lai, J., Feng, Z., Xie, X.: Seeing like a human: asynchronous learning with dynamic progressive refinement for person re-identification. IEEE Trans. Image Process. 31, 352–365 (2021)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Dosovitskiy, A., et al.: An image is worth \(16\times 16\) words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020)
He, S., Luo, H., Wang, P., Wang, F., Li, H., Jiang, W.: TransReID: transformer-based object re-identification. arXiv preprint arXiv:2102.04378 (2021)
Li, Y., He, J., Zhang, T., Liu, X., Zhang, Y., Wu, F.: Diverse part discovery: occluded person re-identification with part-aware transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2898–2907 (2021)
Cai, Y., Takala, V., Pietikainen, M.: Matching groups of people by covariance descriptor. In: 2010 20th International Conference on Pattern Recognition, pp. 2744–2747. IEEE (2010)
Lisanti, G., Martinel, N., Bimbo, A.D., Foresti, G.L.: Group re-identification via unsupervised transfer of sparse features encoding. In: 2017 IEEE International Conference on Computer Vision (ICCV) (2017)
Xiao, H., et al.: Group re-identification: leveraging and integrating multi-grain information. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 192–200 (2018)
Huang, Z., Wang, Z., Hu, W., Lin, C.W., Satoh, S.: DoT-GNN: domain-transferred graph neural network for group re-identification. In: The 27th ACM International Conference (2019)
Huang, Z., Wang, Z., Tsai, C.C., Satoh, S., Lin, C.W.: DotSCN: group re-identification via domain-transferred single and couple representation learning. IEEE Trans. Circ. Syst. Video Technol. 31(7), 2739–2750 (2020)
Mei, L., Lai, J., Feng, Z., Xie, X.: From pedestrian to group retrieval via siamese network and correlation. Neurocomputing 412, 447–460 (2020). https://doi.org/10.1016/j.neucom.2020.06.055
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 (2016)
Lin, W., et al.: Group reidentification with multigrained matching and integration. IEEE Trans. Cybern. 51(3), 1478–1492 (2019)
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
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014)
Zhu, F., Chu, Q., Yu, N.: Consistent matching based on boosted salience channels for group re-identification, pp. 4279–4283, September 2016. https://doi.org/10.1109/ICIP.2016.7533167
Acknowledgments
This project was supported by the NSFC (62076258, 61902444), and the Project of Natural Resources Department of Guangdong Province ([2021]34).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Deng, K., Feng, Z., Lai, JH. (2022). Learning Adaptive Progressive Representation for Group Re-identification. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13534. Springer, Cham. https://doi.org/10.1007/978-3-031-18907-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-18907-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-18906-7
Online ISBN: 978-3-031-18907-4
eBook Packages: Computer ScienceComputer Science (R0)