Abstract
Person re-identification aims to match individual across non-overlapping camera networks. In this paper, we propose a weighted local metric learning (WLML) method for person re-identification. Motivated by the fact that local metric learning has been exploited to handle the data which varies locally, we break down the pedestrian images into several local sub-regions, among which different metric functions are learned. Then we use structured method to learn the weight for each metric function and the final distance is calculated from a weighted sum of these metric functions. Our approach can also combine the local metric functions with global metric functions to exploit their complementary strengths. Moreover it is possible to integrate multiple visual features to further promote the recognition rate. Experiments on two challenging datasets validate the effectiveness of our proposed method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, D., Yuan, Z., Chen, B., Zheng, N.: Similarity learning with spatial constraints for person re-identification. In: CVPR, pp. 1268–1277 (2016)
Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: CVPR, pp. 2197–2206 (2015)
Paisitkriangkrai, S., Shen, C., Hengel, A.V.D.: Learning to rank in person re-identification with metric ensembles. In: CVPR, pp. 1846–1855 (2015)
Xiong, F., Gou, M., Camps, O., Sznaier, M.: Person re-identification using kernel-based metric learning methods. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VII. LNCS, vol. 8695, pp. 1–16. Springer, Heidelberg (2014)
Zhao, R., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: CVPR, pp. 3586–3593 (2013)
Liong, V.E., Lu, J., Ge, Y.: Regularized Bayesian metric learning for person re-identification. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014 Workshops. LNCS, vol. 8927, pp. 209–224. Springer, Heidelberg (2015)
Ma, B., Su, Y., Jurie, F.: Covariance descriptor based on bio-inspired features for person re-identification and face verification. Image Vis. Comput. 32(6–7), 379–390 (2014)
Ma, B., Su, Y., Jurie, F.: Local descriptors encoded by fisher vectors for person re-identification. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012 Ws/Demos, Part I. LNCS, vol. 7583, pp. 413–422. Springer, Heidelberg (2012)
Pedagadi, S., Orwell, J., Velastin, S., Boghossian, B.: Local fisher discriminant analysis for pedestrian re-identification. In: CVPR, pp. 3318–3325 (2013)
Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: PETS (2007)
Zheng, W.S., Gong, S., Xiang, T.: Associating groups of people. In: BMVC, pp. 1–11 (2009)
Joachims, T., Finley, T., Yu, C.-N.J.: Cutting-plane training of structural svms. Mach. Learn. 77, 27–59 (2009)
Liao, S., Zhao, G., Kellokumpu, V., Pietikäinen, M., Li, S.Z.: Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: CVPR (2010)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Gu, X., Ge, Y. (2016). Weighted Local Metric Learning for Person Re-identification. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_75
Download citation
DOI: https://doi.org/10.1007/978-3-319-46654-5_75
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46653-8
Online ISBN: 978-3-319-46654-5
eBook Packages: Computer ScienceComputer Science (R0)