SCIENCE CHINA Information Sciences, Volume 62 , Issue 9 : 199101(2019) https://doi.org/10.1007/s11432-018-9639-7

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

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  • ReceivedMar 27, 2018
  • AcceptedOct 11, 2018
  • PublishedJun 12, 2019


There is no abstract available for this article.


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. ZQN-PY418, ZQN-YX403), and in part by Scientific Research Funds of Huaqiao University (Grant No. 16BS108).


Detailed parameter configuration of the proposed method and performance comparison results.


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  • Figure 1

    (Color online) Diagram for the proposed method. The acronyms MP, HAP, VAP, T, SN and CAT denote max pooling, horizontal average pooling, vertical average pooling, transposition, spatial normalization and concatenation layers, respectively. (a) The packaged block of convolutional layer, batch normalization and leaky ReLU layers (CBLR) block;protect łinebreak (b) the short and dense unit (SDU); (c) the framework of joint horizontal and vertical deep feature learning.