Abstract
Person re-identification (re-id) is an essential task in video surveillance. Existing approaches mainly concentrate on extracting useful appearance features from deep convolutional neural networks. However, they don’t utilize or only partially utilize semantic information such as attributes or person orientation. In this paper, we propose a novel deep neural network framework that greatly improves the accuracy of person re-id and also that of attribute classification. The proposed framework includes two branches, the identity one and the attribute one. The identity branch employs the refined triplet loss and exploits local cues from different regions of the pedestrian body. The attribute branch has an effective attribute predictor containing hierarchical attribute loss functions. After training the identification and attribute classifications, pedestrian representations are derived which contains hierarchical attribute information. The experimental results on DukeMTMC-reID and Matket-1501 datasets validate the effectiveness of the proposed framework in both person re-id and attribute classification. For person re-id, the Rank-1 accuracy is improved by 7.99% and 2.76%, and the mAP is improved by 14.72% and 5.45% on DukeMTMC-reID and Market-1501 datasets respectively. Specifically, it yields 90.95% in accuracy of attribute classification on DukeMTMC-reID, which outperforms the state-of-the-art attribute classification methods by 3.42%.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Meng, X., Leng, B., Song, G.: A Multi-level Weighted Representation for Person Re-identification. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds.) ICANN 2017. LNCS, vol. 10614, pp. 80–88. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68612-7_10
Yi, D., Lei, Z., Liao, S., Li, S. Z.: Deep metric learning for person re-identification. In: Pattern Recognition (ICPR), 22nd International Conference on 2014, pp. 34–39. IEEE (2014)
Zhao, H., et al.: Spindle net: person re-identification with human body region guided feature decomposition and fusion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1077–1085 (2017)
Zhang, X., Pala, F., Bhanu, B.: Attributes co-occurrence pattern mining for video-based person re-identification. In: Advanced Video and Signal Based Surveillance (AVSS), (2017) 14th IEEE International Conference on 2017, pp. 1–6. IEEE (2017)
Matsukawa, T., Suzuki, E.: Person re-identification using CNN features learned from combination of attributes. In: Pattern Recognition (ICPR), 23rd International Conference on 2016 , pp. 2428–2433. IEEE (2016)
Lin, Y., Zheng, L., Zheng, Z., Wu, Y., Yang, Y.: Improving person re-identification by attribute and identity learning. arXiv preprint arXiv:1703.07220 (2017)
Schumann, A., Stiefelhagen, R.: Person re-identification by deep learning attribute-complementary information. In: Computer Vision and Pattern Recognition Work-shops (CVPRW), IEEE Conference on 2017, pp. 1435–1443. IEEE (2017)
Zajdel, W., Zivkovic, Z., Krose, B.: Keeping track of humans: have I seen this person before? In: Proceedings of the 2005 IEEE International Conference on 2005 Robotics and Automation, ICRA 2005, pp. 2081–2086. IEEE (2005)
Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2197–2206 (2015)
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)
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)
Su, C., Zhang, S., Xing, J., Gao, W., Tian, Q.: Multi-type attributes driven multi-camera person re-identification. Pattern Recogn. 75, 77–89 (2018)
Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: CVPR, vol.1, p. 7 (2017)
He, K., Wang, Z., Fu, Y., Feng, R., Jiang, Y.G., Xue, X.: Adaptively weighted multi-task deep network for person attribute classification. In: Proceedings of the 2017 ACM on Multimedia Conference, pp. 1636–1644. ACM (2017)
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, p. 3 (2017)
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)
Zheng, Z., Zheng, L., Yang, Y.: A discriminatively learned CNN embedding for person re-identification. ACM Trans. Multimedia Comput., Commun. Appl. (TOMM) 14(1), 13 (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, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. arXiv preprint arXiv:1701.077173 (2017)
Kurnianggoro, L., Jo, K.H.: Identification of pedestrian attributes using deep network. In: IECON 2017 - Conference of the IEEE Industrial Electronics Society, pp. 8503–8507. IEEE (2017)
Acknowledgment
This work is supported by the Natural Science Foundation of China under Grant No. 61572061, 61472020, 61502020, and the China Postdoctoral Science Foundation under Grant No. 2013M540039.
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
Sun, C., Jiang, N., Zhang, L., Wang, Y., Wu, W., Zhou, Z. (2018). Unified Framework for Joint Attribute Classification and Person Re-identification. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_63
Download citation
DOI: https://doi.org/10.1007/978-3-030-01418-6_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01417-9
Online ISBN: 978-3-030-01418-6
eBook Packages: Computer ScienceComputer Science (R0)