Abstract
Person re-identification (re-ID) across cameras is a crucial task, especially when cameras’ fields of views are non-overlapping. Feature extraction is challenging due to changing illumination conditions, complex background clutters, various camera viewing angles, and occlusions in this case. Moreover, the space mis-alignment of human corresponding regions caused by detectors is a big issue for feature matching across views. In this paper, we propose a strategy of merging attention models with the resnet-50 network for robust feature learning. The efficient self-attention model is used directly on the feature map to solve the space mis-alignment and local feature dependency problems. Furthermore, the loss function which jointly considers the cross-entropy loss and the triplet loss in training enables the network to capture both invariant features within the same individual and distinctive features between different people. Extensive experiments show that our proposed mechanism outperforms the state-of-the-art approaches on the large-scale datasets Market-1501 and DukeMTMC-reID.
Y. Li and X. Jiang contribute equally to this work.
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References
Chen, Y., Zhu, X., Gong, S.: Person re-identification by deep learning multi-scale representations. In: CVPR (2017)
Geng, M., Wang, Y., Xiang, T., Tian, Y.: Deep transfer learning for person reidentification. IEEE TIP (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2015)
Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person reidentification. arXiv:1703.07737 (2017)
Li, W., Zhu, X., Gong, S.: Person re-identification by deep joint learning of multi-loss classification. In: IJCAI (2017)
Li, W., Zhu, X., Gong, S.: Harmonious attention network for person re-identification. In: CVPR (2018)
Lin, Y., Zheng, L., Zheng, Z., Wu, Y., Yang, Y.: Improving person re-identification by attribute and identity learning. In: CVPR (2017)
Luo, H., Gu, Y., Liao, X., Lai, S., Jiang, W.: Bag of tricks and a strong baseline for deep person re-identification. In: CVPR (2019)
Ma, X., et al.: Personre-identification by unsupervised video matching. Pattern Recogn. 65, 197–210 (2017)
Qian, X., et al.: Pose-normalized image generation for person re-identification. In: CVPR (2018)
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
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015)
Schumann, A., Stiefelhagen, R.: Person re-identification by deep learning attribute-complementary information. In: CVPRW (2017)
Su, C., Li, J., Zhang, S., Xing, J., Gao, W., Tian, Q.: Pose-driven deep convolutional model for person re-identification. In: ICCV (2017)
Sun, Y., Zheng, L., Deng, W., Wang, S.: Svdnet for pedestrian retrieval. In: ICCV (2017)
Varior, R.R., Haloi, M., Wang, G.: Gated siamese convolutional neural network architecture for human re-identification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 791–808. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_48
Vaswani, A., et al.: Attention is all you need. arXiv:1706.03762 (2017)
Wang, H., Zhu, X., Xiang, T., Gong, S.: Towards unsupervised open-set person re-identification. In: ICIP (2016)
Wei, L., Zhang, S., Gao, W., Tian, Q.: Person transfer gan to bridge domain gap for person re-identification. In: CVPR (2018)
Xiao, Q., Luo, H., Zhang, C.: Margin sample mining loss: a deep learning based method for person re-identification. arXiv:1710.00478 (2017)
Xiao, T., Li, S., Wang, B., Lin, L., Wang, X.: Joint detection and identification feature learning for person search. In: CVPR (2017)
Yu, H., W.Zheng, A, Wu, Guo, X., Gong, S., Lai, J.: Unsupervised person re-identification by soft multilabel learning. In: CVPR (2019)
Zhang, Y., Xiang, T., Hospedales, T.M., Lu, H.: Deep mutual learning. arXiv:1706.00384 (2017)
Zhao, H., et al.: Spindle net: person re-identification with human body region guided feature decomposition and fusion. In: CVPR (2017)
Zheng, L., Huang, Y., Lu, H., Yang, Y.: Pose invariant embedding for deep person reidentification. arXiv:1701.07732 (2017)
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: ICCV (2015)
Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In: ICCV (2017)
Zheng, Z., Zheng, L., Yang, Y.: Pedestrian alignment network for large-scale person re-identification. IEEE Trans. Circ. Syst. Video Technol. (2018)
Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding. In: CVPR (2017)
Zhong, Z., Zheng, L., Zheng, Z.: Camera style adaptation for person re-identification. In: CVPR (2018)
Acknowledgments
The work is supported by the following projects: National Natural Science Foundation of China, Nr.: 61702322, 6177051715, 61831018.
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Li, Y., Jiang, X., Hwang, JN. (2019). Discriminant Feature Learning with Self-attention for Person Re-identification. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_2
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DOI: https://doi.org/10.1007/978-3-030-36802-9_2
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