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VRSDNet: vehicle re-identification with a shortly and densely connected convolutional neural network

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Abstract

Vehicle re-identification aiming to match vehicle images captured by different cameras plays an important role in video surveillance for public security. In this paper, we solve Vehicle Re-identification with a Shortly and Densely connected convolutional neural Network (VRSDNet). The proposed VRSDNet mainly consists of a list of short and dense units (SDUs), necessary pooling and spatial normalization layers. Specifically, each SDU contains a short list of densely connected convolutional layers and each convolutional layer is of the same appropriate channels. As a result, the number of connections and the input channel of each convolutional layer are restricted in each SDU, and the architecture of VRSDNet is simple. Extensive experiments on both VeRi and VehicleID datasets show that the proposed VRSDNet is obviously superior to multiple state-of-the-art vehicle re-identification methods in terms of accuracy and speed.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under the Grants 61602191, 61672521, 61375037, 61473291, 61572501, 61572536, 61502491, 61372107 and 61401167, in part by the Natural Science Foundation of Fujian Province under the Grants 2018J01090 and 2016J01308, in part by High-level Talent Innovation Program of Quanzhou City under the Grants 2017G027 and 2017G036, in part by the Scientific and Technology Founds of Xiamen under the Grant 3502Z20173045, in part by the Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University under the Grants ZQN-PY418 and ZQN-YX403, and in part by the Scientific Research Funds of Huaqiao University under the Grant 16BS108.

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Authors and Affiliations

  1. College of Engineering, Huaqiao University, 269 Chenghua North Road, Quanzhou, China

    Jianqing Zhu, Yongzhao Du, Lixin Zheng & Canhui Cai

  2. First Research Institute of the Ministry of Public Security of People’s Republic of China, 1 Shouti South Road, 100048, Beijing, China

    Yang Hu

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  1. Jianqing Zhu
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  2. Yongzhao Du
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  3. Yang Hu
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  4. Lixin Zheng
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  5. Canhui Cai
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Correspondence to Jianqing Zhu.

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Zhu, J., Du, Y., Hu, Y. et al. VRSDNet: vehicle re-identification with a shortly and densely connected convolutional neural network. Multimed Tools Appl 78, 29043–29057 (2019). https://doi.org/10.1007/s11042-018-6270-4

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