Abstract
Cross-camera pedestrian re-identification (re-ID) is of paramount importance for surveillance tasks. Although considerable progress has been made to improve the re-ID accuracy, real-time pedestrian detection and re-ID remains a challeging problem. In this work, first, we proposed an enhanced aggregated channel features (ACF+) based on the ACF pedestrian detector [1] for real-time pedestrian detection and re-ID; Second, to further improve the representation power of the combined multiple channel features, we proposed a novel hybrid distance metric learning method. Extensive experiments have been carried on two public datasets, including VIPeR, and PRID2011. The experimental results show that our proposed method can achieve state-of-the-art accuracy while being computational efficient for real-time applications. The proposed hybrid distance metric learning is general, thus can be applied to any metric learning approaches.
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
Appel, R., Belongie, S., Perona, P., Doll, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1532–1545 (2014)
Chen, S.Z., Guo, C.C., Lai, J.H.: Deep ranking for person re-identification via joint representation learning. IEEE Trans. Image Process. 25(5), 2353–2367 (2016)
Yi, D., Lei, Z., Liao, S., Li, S.Z.: Deep metric learning for person re-identification. In: International Conference on Pattern Recognition (2014)
Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: IEEE Conference on Computer Vision and Pattern Recognition (2010)
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. LNCS, vol. 8695, pp. 1–16. Springer, Cham (2014). doi:10.1007/978-3-319-10584-0_1
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). doi:10.1007/978-3-642-21227-7_9
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). doi:10.1007/978-3-642-33783-3_56
Kostinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)
Li, W., Wang, X.: Locally aligned feature transforms across views. In: IEEE Conference on Computer Vision and Pattern Recognition (2013)
Liao, S., Zhao, G., Kellokumpu, V., Pietikainen, M., Li, S.Z.: Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (2010)
Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: IEEE Conference on Computer Vision and Pattern Recognition (2015)
Liao, S., Li, S.Z.: Efficient PSD constrained asymmetric metric learning for person re-identification. In: International Conference on Computer Vision (2015)
Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: Proceedings of IEEE International Workshop on PETS (2007)
Xu, Y., Lin, L., Zheng, W.S., Liu, X.: Human re-identification by matching compositional template with cluster sampling. In: International Conference on Computer Vision (2013)
Yang, Y., Liao, S., Lei, Z., Li, S.Z.: Large scale similarity learning using similar pairs for person verification. In: AAAI (2016)
Zhao, R., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition (2013)
Acknowledgments
This work is supported by National Natural Science Foundation of China (project no. 6160011396).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Huang, X., Xu, J., Guo, G., Zheng, E. (2017). Hybrid Distance Metric Learning for Real-Time Pedestrian Detection and Re-identification. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-68345-4_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68344-7
Online ISBN: 978-3-319-68345-4
eBook Packages: Computer ScienceComputer Science (R0)