Abstract
We focus on the person re-identification (re-id) problem of matching people across non-overlapping camera views. While most existing works rely on the abundance of labeled exemplars, we consider a more difficult unsupervised scenario, where no labeled exemplar is provided. One solution for unsupervised re-id that attracts much attention in the recent researches is cross-dataset transfer learning. It utilizes knowledge from multiple source datasets from different domains to enhance the unsupervised learning performance on the target domain. In previous works, much effect is taken on extraction of the generic and robust common appearances representations across domains. However, we observe that there also particular appearances in different domains. Simply ignoring these domain-unique appearances will misleading the matching schema in re-id application. Few unsupervised cross-dataset algorithms are proposed to learn the common appearances across multiple domains, even less of them consider the domain-unique representations. In this paper, we propose a novel domain-aware representation learning algorithm for unsupervised cross-dataset person re-id problem. The proposed algorithm not only learns a common appearances across-datasets but also captures the domain-unique appearances on the target dataset via minimization of the overlapped signal supports across different domains. Extensive experimental studies on benchmark datasets show superior performances of our algorithm over state-of-the-art algorithms. Sample analysis on selected samples also verifies the ability of diversity learning of our algorithm.
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
Candes, E.J., Wakin, M.B., Boyd, S.P.: Enhancing sparsity by reweighted \(\text{ ell }\,\, \_1\) minimization. J. Fourier Anal. Appl. 14(5–6), 877–905 (2008)
Chartrand, R., Yin, W.: Iteratively reweighted algorithms for compressive sensing. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2008, pp. 3869–3872. IEEE (2008)
Cheng, D.S., Cristani, M., Stoppa, M., Bazzani, L., Murino, V.: Custom pictorial structures for re-identification. In: BMVC (2011)
Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: CVPR, pp. 2360–2367. IEEE (2010)
Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: PETS, vol. 3 (2007)
Guo, H., Jiang, Z., Davis, L.S.: Discriminative dictionary learning with pairwise constraints. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7724, pp. 328–342. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37331-2_25
Hirzer, M., Beleznai, C., Roth, P.M., Bischof, H.: Person re-identification by descriptive and discriminative classification. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 91–102. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21227-7_9
Karanam, S., Li, Y., Radke, R.J.: Person re-identification with discriminatively trained viewpoint invariant dictionaries. In: ICCV, pp. 4516–4524 (2015)
Kodirov, E., Xiang, T., Fu, Z., Gong, S.: Person re-identification by unsupervised\(\backslash \)\(\text{ ell } \_1\) graph learning. In: ECCV. pp. 178–195. Springer (2016)
Kodirov, E., Xiang, T., Gong, S.: Dictionary learning with iterative laplacian regularisation for unsupervised person re-identification. In: BMVC, vol. 3, p. 8 (2015)
Li, S., Shao, M., Fu, Y.: Cross-view projective dictionary learning for person re-identification. In: IJCAI, pp. 2155–2161 (2015)
Li, W., Zhao, R., Xiao, T., Wang, X.: Deepreid: deep filter pairing neural network for person re-identification. In: CVPR (2014)
Lisanti, G., Masi, I., Bagdanov, A., Del Bimbo, A.: Person re-identification by iterative re-weighted sparse ranking. IEEE Trans. Pattern Anal. Mach. Intell. PP(99), 1 (2014). https://doi.org/10.1109/TPAMI.2014.2369055
Lisanti, G., Masi, I., Bagdanov, A.D., Del Bimbo, A.: Person re-identification by iterative re-weighted sparse ranking. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 1629–1642 (2015)
Lisanti, G., Masi, I., Del Bimbo, A.: Matching people across camera views using kernel canonical correlation analysis. In: Proceedings of the International Conference on Distributed Smart Cameras, p. 10. ACM (2014)
Liu, Y., Chen, W., Chen, Q., Wassell, I.: Support discrimination dictionary learning for image classification. In: ECCV. pp. 375–390. Springer (2016)
Lv, J., Chen, W., Li, Q., Yang, C.: Unsupervised cross-dataset person re-identification by transfer learning of spatial-temporal patterns. arXiv preprint arXiv:1803.07293 (2018)
Ma, A.J., Li, J., Yuen, P.C., Li, P.: Cross-domain person reidentification using domain adaptation ranking SVMs. IEEE Trans. Image Process. 24(5), 1599–1613 (2015)
Peng, P., et al.: Unsupervised cross-dataset transfer learning for person re-identification. In: CVPR, pp. 1306–1315 (2016)
Rubinstein, R., Zibulevsky, M., Elad, M.: Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit. Cs Technion 40(8), 1–15 (2008)
Wang, H., Gong, S., Xiang, T.: Unsupervised learning of generative topic saliency for person re-identification. In: Proceedings of the British Machine Vision Conference. BMVA Press (2014)
Wang, J., Zhu, X., Gong, S., Li, W.: Transferable joint attribute-identity deep learning for unsupervised person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2275–2284 (2018)
Wipf, D., Nagarajan, S.: Iterative reweighted \(\ell _1\) and \(\ell _2\) methods for finding sparse solutions. IEEE J. Sel. Top. Signal Process. 4(2), 317–329 (2010)
Xiao, T., Li, H., Ouyang, W., Wang, X.: Learning deep feature representations with domain guided dropout for person re-identification. In: CVPR, pp. 1249–1258 (2016)
Yu, H.X., Wu, A., Zheng, W.S.: Cross-view asymmetric metric learning for unsupervised person re-identification. In: ICCV (2017)
Zhang, Y., Yeung, D.Y.: A convex formulation for learning task relationships in multi-task learning. arXiv preprint arXiv:1203.3536 (2012)
Zhao, R., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: CVPR, pp. 3586–3593 (2013)
Zheng, M., et al.: Graph regularized sparse coding for image representation. IEEE Trans. Image Process. 20(5), 1327–1336 (2011)
Acknowledgements
This paper is based on research sponsored by Air Force Research Laboratory and DARPA under agreement number FA8750-19-2-0501 and ARC DECRA (DE190100626). The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. This project is also aligned with and co-sponsored by the Disaster Management and Smart Cities related activities in CSIRO Data61.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, Z., Liu, W., Chang, X., Yao, L., Prakash, M., Zhang, H. (2019). Domain-Aware Unsupervised Cross-dataset Person Re-identification. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-35231-8_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35230-1
Online ISBN: 978-3-030-35231-8
eBook Packages: Computer ScienceComputer Science (R0)