Abstract
Person re-identification task aims at matching pedestrian images across multiple camera views. Extracting more robust feature of the pedestrian images and finding more discriminative metric learning are the main research directions in person re-identification. The achieved results are provided in the form of a list of ranked matching persons. It often happens that the true match which should be in the first position is not ranked first. In order to correct some false matches and improve the accuracy of person re-identification, this paper proposes a re-ranking method with forward and reverse sorting constraints. The forward sorting constraint makes the image, which is in the front position of one forward sorting list, be backward in the position of other forward sorting lists; The reverse sorting constraint makes two images of the same pedestrian be in the front position of each other’s sorting list. Experiments on four public person re-identification datasets, VIPeR, PRID450S, CUHK01 and CUHK03 confirm the simplicity and effectiveness of our method.
Supported by organization by the National Natural Science Foundation of China Grant 61632007 and Key Research and Development Project of Anhui Province, China 1704d0802183.
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
Chen, D., Yuan, Z., Chen, B., Zheng, N.: Similarity learning with spatial constraints for person re-identification. In: Computer Vision and Pattern Recognition, pp. 1268–1277 (2016)
Garcia, J., Martinel, N., Gardel, A., Bravo, I., Foresti, G.L., Micheloni, C.: Discriminant context information analysis for post-ranking person re-identification. IEEE Trans. Image Process. 26(4), 1650–1665 (2017)
Garcia, J., Martinel, N., Micheloni, C., Gardel, A.: Person re-identification ranking optimisation by discriminant context information analysis. In: IEEE International Conference on Computer Vision, pp. 1305–1313 (2015)
Gray, D., Tao, H.: Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features. Springer, Heidelberg (2008)
Hirzer, M.: Large scale metric learning from equivalence constraints. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2288–2295 (2012)
Leng, Q., Hu, R., Liang, C., Wang, Y., Chen, J.: Person re-identification with content and context re-ranking. Multimedia Tools Appl. 74(17), 6989–7014 (2015)
Li, W., Wu, Y., Mukunoki, M., Minoh, M.: Common-near-neighbor analysis for person re-identification. In: IEEE International Conference on Image Processing, pp. 1621–1624 (2013)
Li, W., Zhao, R., Wang, X.: Human Reidentification with Transferred Metric Learning. Springer, Heidelberg (2013)
Li, W., Zhao, R., Xiao, T., Wang, X.: DeepReID: deep filter pairing neural network for person re-identification. In: Computer Vision and Pattern Recognition, pp. 152–159 (2014)
Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: Computer Vision and Pattern Recognition, pp. 2197–2206 (2015)
Liao, S., Li, S.Z.: Efficient PSD constrained asymmetric metric learning for person re-identification. In: IEEE International Conference on Computer Vision, pp. 3685–3693 (2015)
Lisanti, G., Masi, I., Bimbo, A.D.: Matching people across camera views using kernel canonical correlation analysis, pp. 1–6 (2014)
Liu, C., Chen, C.L., Gong, S., Wang, G.: POP: person re-identification post-rank optimisation. In: IEEE International Conference on Computer Vision, pp. 441–448 (2014)
Liu, C., Gong, S., Chen, C.L.: On-the-fly feature importance mining for person re-identification. Patt. Recogn. 47(4), 1602–1615 (2014)
Matsukawa, T., Okabe, T., Suzuki, E., Sato, Y.: Hierarchical Gaussian descriptor for person re-identification. In: Computer Vision and Pattern Recognition, pp. 1363–1372 (2016)
Nguyen, N.B., Nguyen, V.H., Ngo, T.D., Nguyen, K.M.T.T.: Person re-identification with mutual re-ranking. Vietnam J. Comput. Sci. 4, 1–12 (2017)
Nguyen, V.H., Ngo, T.D., Nguyen, K.M.T.T., Duong, D.A., Nguyen, K., Le, D.D.: Re-ranking for person re-identification. In: Soft Computing and Pattern Recognition, pp. 304–308 (2015)
Roth, P.M., Hirzer, M., Köstinger, M., Beleznai, C., Bischof, H.: Mahalanobis distance learning for person re-identification, pp. 247–267 (2014)
Xie, Y., Yu, H., Gong, X., Levine, M.D.: Adaptive metric learning and probe-specific reranking for person reidentification. IEEE Sig. Process. Lett. 24(6), 853–857 (2017)
Ye, M., et al.: Person reidentification via ranking aggregation of similarity pulling and dissimilarity pushing. IEEE Trans. Multimedia 18(12), 2553–2566 (2016)
You, J., Wu, A., Li, X., Zheng, W.S.: Top-push video-based person re-identification, pp. 1345–1353 (2016)
Zheng, L., Wang, S., Tian, L., He, F., Liu, Z., Tian, Q.: Query-adaptive late fusion for image search and person re-identification. In: Computer Vision and Pattern Recognition, pp. 1741–1750 (2015)
Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding, pp. 3652–3661 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Qi, M., Wei, Y., Gao, K., Jiang, J., Wu, J. (2018). Re-Ranking Person Re-Identification with Forward and Reverse Sorting Constraints. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_71
Download citation
DOI: https://doi.org/10.1007/978-3-030-00767-6_71
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00766-9
Online ISBN: 978-3-030-00767-6
eBook Packages: Computer ScienceComputer Science (R0)