Abstract
Person re-identification (re-ID), which aims to re-identify a person captured by one camera from another camera at any non-overlapping location, has attracted more and more attention in recent years. So far, it has been significantly improved by deep learning technology. A variety of deep models have been proposed in person re-ID community. In order to make the deep model simple and effective, we propose an identification model that combines the softmax loss with center loss. Moreover, various data augmentation methods and re-ranking strategy are used to improve the performance of the proposed model. Experiments on CUHK03 and Market-1501 datasets demonstrate that the proposed model is effective and has good results in most cases.
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References
Huang, D.S.: Systematic Theory of Neural Networks for Pattern Recognition. Publishing House of Electronic Industry of China (1996). (in Chinese)
Yi, D., et al.: Deep metric learning for person re-identification. In: 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, pp. 34–39 (2014)
Jin, H., et al.: Deep Person Re-Identification with Improved Embedding and Efficient Training (2017)
Xiao, T., et al.: Joint Detection and Identification Feature Learning for Person Search (2017)
Xiao, T., et al.: Learning deep feature representations with domain guided dropout for person re-identification. In: Computer Vision and Pattern Recognition, pp. 1249–1258 (2016)
Huang, D.S.: Radial basis probabilistic neural networks: model and application. Int. J. Pattern Recognit. Artif. Intell. 13(07), 1083–1101 (1999)
Zhao, Z.Q., Huang, D.S., et al.: Human face recognition based on multi-features using neural networks committee. Pattern Recogn. Lett. 25(12), 1351–1358 (2004)
Huang, D.S., Zhao, W.B.: Determining the Centers of Radial Basis Probabilistic Neural Networks by Recursive Orthogonal Least Square Algorithms ☆. Elsevier Science Inc. (2005)
Huang, D.S., Du, J.-X.: A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks. IEEE Trans. Neural Networks 19(12), 2099–2115 (2008)
Zhong, Z., et al.: Random erasing data augmentation (2017)
Wei, L., et al.: Person transfer GAN to bridge domain gap for person re-identification (2017)
Wen, Y., Zhang, K., Li, Z., Qiao, Yu.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_31
Zhong, Z., et al.: Re-ranking person re-identification with k-reciprocal encoding. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3652–3661 (2017)
Huang, D.S., Jiang, W.: A general CPL-AdS methodology for fixing dynamic parameters in dual environments. IEEE Trans. Syst. Man Cybern. Part B 42(5), 1489–1500 (2012)
Schumann, A., Stiefelhagen, R.: Person re-identification by deep learning attribute-complementary information. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1435–1443 (2017)
Varior, R.R., Haloi, M., Wang, G.: Gated Siamese Convolutional Neural Network Architecture for Human Re-identification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 791–808. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_48
Acknowledgments
This work was supported by the grants of the National Science Foundation of China, Nos. 61472280, 61672203, 61472173, 61572447, 61772357, 31571364, 61520106006, 61772370, 61702371 and 61672382, China Postdoctoral Science Foundation Grant, Nos. 2016M601646 & 2017M611619, and supported by “BAGUI Scholar” Program of Guangxi Zhuang Autonomous Region of China. De-Shuang Huang is the corresponding author of this paper.
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Zheng, SJ. et al. (2018). A Simple and Effective Deep Model for Person Re-identification. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_24
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DOI: https://doi.org/10.1007/978-3-319-95957-3_24
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