Abstract
The standard person re-identification task has a basic assumption that pedestrians will not change their clothes. However, in a more realistic and challenging scenario for clothes-changing person re-identification, this assumption does not hold, resulting in the failure of most mainstream methods in this scenario. To this end, a double-stream network for clothes-changing person re-identification based on clothes related feature suppression and attention mechanism is proposed. Firstly, the network is composed of a clothes feature stream and a pedestrian feature stream. By using attention modules on the two streams, salient clothes features and pedestrian features are extracted respectively. Then, the clothes related feature suppression module is used in the pedestrian feature stream to force it to learn clothes unrelated features. Finally, the triplet loss and cross entropy loss are used to supervise the training of the network. The ablation experiment shows that each module effectively improves the performance of the model. A large number of experiments on the PRCC and VC-Clothes datasets have been conducted in order to assess the proposed model. The experimental results show that the accuracy of mAP and Rank-1 is improved compared with other representative methods.
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
Zhang, Z., et al.: Gait recognition via disentangled representation learning. In: CVPR (2019)
Zheng, Z., Yang, X., Yu, Z., Zheng, L., Yang, Y., Kautz, J.: Joint discriminative and generative learning for person re-identification. In: CVPR (2019)
Gu, X., Chang, H., Ma, B., Bai, S., Shan, S., Chen, X.: Clothes-changing person re-identification with RGB modality only. In: CVPR (2022)
Hong, P., Wu, T., Wu, A., Han, X., Zheng, W.S.: Fine-grained shape-appearance mutual learning for cloth-changing person re-identification. In: CVPR (2021)
Jin, X., et al.: Cloth-changing person reidentification from a single image with gait prediction and regularization. arXiv preprint arXiv:2103.15537 (2021)
Qian, X., et al.: Long-term cloth-changing person re-identification. In: ACCV (2020)
Yang, Q., Ancong, W., Zheng, W.-S.: Person reidentification by contour sketch under moderate clothing change. TPAMI 43(6), 2029–2046 (2021)
Chen, J., et al.: Learning 3d shape feature for texture-insensitive person re-identification. In: CVPR (2021)
Gu, K., Xia, Z., Qiao, J., et al.: Deep dual-channel neural network for image-based smoke detection. IEEE Trans. Multimed. 22(2), 311–323 (2019). https://doi.org/10.1109/TMM.2019.2929009
Gu, K., et al.: Pm 2.5 monitoring: use information abundance measurement and wide and deep learning. IEEE Trans. Neural Netw. Learn. Syst. 2, 4278–4290 (2021). https://doi.org/10.1109/TNNLS.2021.3105394
Wan, F., Wu, Y., Qian, X., Chen, Y., Fu, Y.: When person re-identification meets changing clothes. In: CVPR Workshop (2020)
Li, W., Zhu, X., Gong, S.: Harmonious attention network for person re-identification. In: CVPR (2018)
Hou, R., Ma, B., Chang, H., Gu, X., Shan, S., Chen, X.: Interaction-and-aggregation network for person re-identification. In: CVPR (2019)
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: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 501–518. Springer, 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: Proceedings of the 26th ACM International Conference on Multimedia, pp. 274–282 (2018)
Fu, Y., et al.: Horizontal pyramid matching for person re-identification. Proc. AAAI Conf. Artif. Intell. (AAAI) 33(01), 8295–8302 (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for largescale image recognition. In: International Conference on Learning Representations (ICLR) (2015)
Huang, Y., Wu, Q., Xu, J., Zhong, Y., Zhang, Z.: Clothing status awareness for long-term person re-identification. In: ICCV (2021)
Qian, X., Fu, Y., Jiang, Y.G., Xiang, T., Xue, X.: Multi-scale deep learning architectures for person re-identification. In: ICCV (2017)
Suh, Y., Wang, J., Tang, S., Mei, T., Lee, K.M.: Part-aligned bilinear representations for person re-identification. In: ECCV (2018)
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
Wang, D., Du, H. (2023). Double-Stream Network for Clothes-Changing Person Re-identification Based on Clothes Related Feature Suppression and Attention Mechanism. In: Yongtian, W., Lifang, W. (eds) Image and Graphics Technologies and Applications. IGTA 2023. Communications in Computer and Information Science, vol 1910. Springer, Singapore. https://doi.org/10.1007/978-981-99-7549-5_16
Download citation
DOI: https://doi.org/10.1007/978-981-99-7549-5_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7548-8
Online ISBN: 978-981-99-7549-5
eBook Packages: Computer ScienceComputer Science (R0)