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Joint horizontal and vertical deep learning feature for vehicle re-identification

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References

  1. Jin X, Zhu S Y, Xiao C E, et al. 3D textured model encryption via 3D Lu chaotic mapping. Sci China Inf Sci, 2017, 60: 122107

    Article  Google Scholar 

  2. Guo L H, Guo C G, Li L, et al. Two-stage local constrained sparse coding for fine-grained visual categorization. Sci China Inf Sci, 2018, 61: 018104

    Article  Google Scholar 

  3. Zhu J Q, Zeng H Q, Liao S C, et al. Deep hybrid similarity learning for person re-identification. IEEE Trans Circ Syst Video Technol, 2018, 28: 3183–3193

    Article  Google Scholar 

  4. Chen Y C, Zheng W S, Lai J H, et al. An asymmetric distance model for cross-view feature mapping in person re-identification. IEEE Trans Circ Syst Video Technol, 2017, 27: 1661–1675

    Article  Google Scholar 

  5. Liao S C, Hu Y, Zhu X Y, et al. Person reidentification by local maximal occurrence representation and metric learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Boston, 2015. 2197–2206

    Google Scholar 

  6. Zhu J Q, Zeng H Q, Du Y Z, et al. Person re-identification based on novel triplet convolutional neural network. J Electron Inf Technol, 2018, 40: 1012–1016

    Google Scholar 

  7. Zhu J Q, Zeng H Q, Lei Z, et al. A shortly and densely connected convolutional neural network for vehicle reidentification. In: Proceedings of International Conference on Pattern Recognition, Beijing, 2018

    Google Scholar 

  8. Zheng L, Wang S J, Zhou W G, et al. Bayes merging of multiple vocabularies for scalable image retrieval. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014. 1963–1970

    Google Scholar 

  9. Liu X C, Liu W, Mei T, et al. PROVID: progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans Multimedia, 2018, 20: 645–658

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61602191, 61871434, 61802136, 61672521), in part by Natural Science Foundation of Fujian Province (Grant Nos. 2018J01090, 2016J01308), in part by Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University (Grant Nos. ZQNPY418, ZQN-YX403), and in part by Scientific Research Funds of Huaqiao University (Grant No. 16BS108).

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Correspondence to Huanqiang Zeng or Xin Jin.

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Zhu, J., Zeng, H., Jin, X. et al. Joint horizontal and vertical deep learning feature for vehicle re-identification. Sci. China Inf. Sci. 62, 199101 (2019). https://doi.org/10.1007/s11432-018-9639-7

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  • DOI: https://doi.org/10.1007/s11432-018-9639-7

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