Abstract
Compared with conventional ReID task, RGB-IR person re-identification is more significant and challenging owing to enormous cross-modality variations between RGB and infrared images. Most existing cross-modality person re-identification approaches in the infrared and visible domains use the joint training of traditional triplet loss and CE loss to reduce the discrepancy between two modalities. However, as the model gradually converges, the number of positive and negative sample pairs that can be optimized by traditional triplet loss also decreases. In this paper, we adopt a dual-stream CNN structure and propose a maximum intra-class distance to triplet loss (MICT loss) for RGB-IR ReID task. The proposed method enjoys several advantages. First, it can learn specific modal information and shared information better through the CNN structure of the dual-stream branch. Second, it can add the optimization of intra-class cross-modality distance to the traditional triplet loss, which can effectively reduce the intra-class distance and increase the training difficulty of the model. Experimental results demonstrate that our proposed algorithm performs favorably against the state-of-the-art methods that rank-1 accuracy is 61.71% and mAP is 58.06% on SYSU-MM01 dataset.
X. Hu—Student.
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References
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT 2010, pp. 177–186. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-7908-2604-3_16
Chen, W., Chen, X., Zhang, J., Huang, K.: Beyond triplet loss: a deep quadruplet network for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 403–412 (2017)
Dai, P., Ji, R., Wang, H., Wu, Q., Huang, Y.: Cross-modality person re-identification with generative adversarial training. In: IJCAI, vol. 1, p. 2 (2018)
Das, A., Chakraborty, A., Roy-Chowdhury, A.K.: Consistent re-identification in a camera network. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 330–345. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10605-2_22
Ding, S., Lin, L., Wang, G., Chao, H.: Deep feature learning with relative distance comparison for person re-identification. Pattern Recogn. 48(10), 2993–3003 (2015)
Hao, Y., Wang, N., Li, J., Gao, X.: HSME: hypersphere manifold embedding for visible thermal person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8385–8392 (2019)
Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)
Hirzer, M., Roth, P.M., Köstinger, M., Bischof, H.: Relaxed pairwise learned metric for person re-identification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 780–793. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33783-3_56
Koestinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2288–2295. IEEE (2012)
Li, S., Xiao, T., Li, H., Yang, W., Wang, X.: Identity-aware textual-visual matching with latent co-attention. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1890–1899 (2017)
Li, S., Xiao, T., Li, H., Zhou, B., Yue, D., Wang, X.: Person search with natural language description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1970–1979 (2017)
Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2197–2206 (2015)
Lin, J.W., Li, H.: Hpiln: a feature learning framework for cross-modality person re-identification. arXiv preprint arXiv:1906.03142 (2019)
Lin, Y., et al.: Improving person re-identification by attribute and identity learning. Pattern Recogn. 95, 151–161 (2019)
Luo, H., Gu, Y., Liao, X., Lai, S., Jiang, W.: Bag of tricks and a strong baseline for deep person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)
Lv, J., Chen, W., Li, Q., Yang, C.: Unsupervised cross-dataset person re-identification by transfer learning of spatial-temporal patterns. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7948–7956 (2018)
Nguyen, D.T., Hong, H.G., Kim, K.W., Park, K.R.: Person recognition system based on a combination of body images from visible light and thermal cameras. Sensors 17(3), 605 (2017)
Wu, A., Zheng, W.S., Yu, H.X., Gong, S., Lai, J.: RGB-infrared cross-modality person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5380–5389 (2017)
Xiao, Q., Luo, H., Zhang, C.: Margin sample mining loss: a deep learning based method for person re-identification. arXiv preprint arXiv:1710.00478 (2017)
Ye, M., Lan, X., Li, J., Yuen, P.C.: Hierarchical discriminative learning for visible thermal person re-identification. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Ye, M., Liang, C., Wang, Z., Leng, Q., Chen, J., Liu, J.: Specific person retrieval via incomplete text description. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 547–550 (2015)
Ye, M., Wang, Z., Lan, X., Yuen, P.C.: Visible thermal person re-identification via dual-constrained top-ranking. In: IJCAI, vol. 1, p. 2 (2018)
Zhang, S., Yang, Y., Wang, P., Zhang, X., Zhang, Y.: Attend to the difference: cross-modality person re-identification via contrastive correlation. arXiv preprint arXiv:1910.11656 (2019)
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 (2015)
Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: past, present and future. arXiv preprint arXiv:1610.02984 (2016)
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Hu, X., Zhou, Y. (2020). Cross-Modality Person ReID with Maximum Intra-class Triplet Loss. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12306. Springer, Cham. https://doi.org/10.1007/978-3-030-60639-8_46
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