Abstract
Person re-identification (person re-ID) is refers to pedestrians matching under a multi camera network in a non-overlapping field of view, that is, whether the pedestrian targets taken by different cameras are the same. In this paper, the image based traditional person re-ID is introduced from feature description and metric learning. In addition, with the extensive application of the deep learning algorithm in recent years, it also brings about the change of person re-ID algorithm. From the three aspects of loss function design, local feature and data augmentation, this paper introduces some work of person re-ID algorithm based on deep learning. Finally, it summarizes the development of pedestrian datasets in recent years, and looks forward to the future development trend of person re-ID.
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References
Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: European Conference on Computer Vision, pp. 262–275. Springer, Berlin (2008)
Prosser, B.J., Zheng, W.S., Gong, S., Xiang, T., Mary, Q.: Person re-identification by support vector ranking. In: BMVC, vol. 2, no. 5, p. 6 (2010)
Mignon, A., Jurie, F.: Pcca: a new approach for distance learning from sparse pairwise constraints. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 2666–2672. IEEE (2012)
Zhao, R., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 3586–3593. IEEE (2013)
Zheng, W.S., Li, X., Xiang, T., Liao, S., Lai, J., Gong, S.: Partial person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4678–4686 (2015)
Gheissari, N., Sebastian, T.B., Hartley, R.: Person re-identification using spatiotemporal appearance. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, vol. 2, pp. 1528–1535. IEEE (2006)
Roth, P.M., Hirzer, M., Köstinger, M., Beleznai, C., Bischof, H.: Mahalanobis distance learning for person re-identification. In: Person Re-identification, pp. 247–267. Springer, London (2014)
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 (2014)
Zheng, L., Bie, Z., Sun, Y., Wang, J., Su, C., Wang, S., Tian, Q.: Mars: a video benchmark for large-scale person re-identification. In: European Conference on Computer Vision, pp. 868–884. Springer, Cham (2016)
Oreifej, O., Mehran, R., Shah, M.: Human identity recognition in aerial images. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010, pp. 709–716. IEEE (2010)
Jüngling, K., Bodensteiner, C., Arens, M.: Person re-identification in multi-camera networks. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2011, pp. 55–61. IEEE (2011)
Tuzel, O., Porikli, F., Meer, P.: Pedestrian detection via classification on Riemannian manifolds. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1713–1727 (2008)
Ma, B., Su, Y., Jurie, F.: Local descriptors encoded by fisher vectors for person re-identification. In: European Conference on Computer Vision, pp. 413–422. Springer, Heidelberg (2012)
Chen, D., Yuan, Z., Hua, G., Zheng, N., Wang, J.: Similarity learning on an explicit polynomial kernel feature map for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1565–1573 (2015)
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)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Yi, D., Lei, Z., Liao, S., Li, S.Z.: Deep metric learning for person re-identification. In: 22nd International Conference on Pattern Recognition (ICPR), 2014, pp. 34–39. IEEE (2014)
Varior, R.R., Haloi, M., Wang, G.: Gated siamese convolutional neural network architecture for human re-identification. In: European Conference on Computer Vision, pp. 791–808. Springer, Cham (2016)
Ahmed, E., Jones, M., Marks, T.K.: An improved deep learning architecture for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3908–3916 (2015)
Zheng, Z., Zheng, L., Yang, Y.: A discriminatively learned CNN embedding for person reidentification. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 14(1), 13 (2017)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Liu, H., Feng, J., Qi, M., Jiang, J., Yan, S.: End-to-end comparative attention networks for person re-identification. IEEE Trans. Image Process. 26(7), 3492–3506 (2017)
Cheng, D., Gong, Y., Zhou, S., Wang, J., Zheng, N.: Person re-identification by multi-channel parts-based cnn with improved triplet loss function. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1335–1344 (2016)
Chen, W., Chen, X., Zhang, J., Huang, K.: Beyond triplet loss: a deep quadruplet network for person re-identification. In: Proceedings of CVPR, vol. 2 (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)
Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)
Zheng, Z., Zheng, L., Yang, Y.: Pedestrian alignment network for large-scale person re-identification. arXiv preprint arXiv:1707.00408 (2017)
Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by gan improve the person re-identification baseline in vitro. arXiv preprint arXiv:1701.07717 (2017)
Qian, X., Fu, Y., Wang, W., Xiang, T., Wu, Y., Jiang, Y.G., Xue, X.: Pose-Normalized Image Generation for Person Re-identification. arXiv preprint arXiv:1712.02225 (2017)
Huang, H.J., Li, D.W., Zhang, Z., Huang, K.Q.: Adversarially occluded samples for improving generalization of person re-identification models. In: CVPR (2018). (in press)
Wei, L., Zhang, S., Gao, W., Tian, Q.: Person Transfer GAN to Bridge Domain Gap for Person Re-identification. arXiv preprint arXiv:1711.08565 (2017)
Deng, W., Zheng, L., Kang, G., Yang, Y., Ye, Q., Jiao, J.: Image-Image Domain Adaptation with Preserved Self-similarity and Domain-Dissimilarity for Person Re-identification. arXiv preprint arXiv:1711.07027 (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 (2015)
Zheng, L., Zhang, H., Sun, S., Chandraker, M., Tian, Q.: Person re-identification in the wild. arXiv preprint arXiv:1604.02531 (2017)
Acknowledgments
This work was supported by the Scientific Research Fund of Hunan Provincial Education Department of China (Project No. 17A007); and the Teaching Reform and Research Project of Hunan Province of China (Project No. JG1615).
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Zhou, S., Ke, M., Luo, P. (2019). An Overview of Image-Based Person Re-identification. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Security and Intelligence ATCI 2018. ATCI 2018. Advances in Intelligent Systems and Computing, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-319-98776-7_39
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DOI: https://doi.org/10.1007/978-3-319-98776-7_39
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