Joint Pyramid Feature Representation Network for Vehicle Re-identification

Abstract

Vehicle re-identification (Re-ID) technology plays an important role in the intelligent transportation system for smart city. Due to various uncertain factors in the real-world scenarios, (e.g., resolution variation, viewpoint variation, illumination changes, occlusion, etc., vehicle Re-ID is a very challenging task. To resist the adverse effect of resolution variation, a joint pyramid feature representation network (JPFRN) for vehicle Re-ID is proposed in this paper. Based on the consideration that various convolution blocks with different depths hold different resolutions and semantic information of the vehicle image, the proposed JPFRN method employs a base network to obtain multi-resolution vehicle features in the first stage. Then, a pyramid feature representation scheme is developed to reconstruct and integrate the obtained multi-resolution vehicle features together. Finally, these pyramid features are jointly represented for learning a more discriminative feature under the supervision of joint Triplet loss and softmax loss. Extensive experimental results on two commonly-used vehicle databases (i.e., VehicleID and VeRi) show that the proposed JPFRN is superior to multiple recently-developed vehicle Re-ID methods.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under the grants 61871434, 61602191, and 61802136, in part by the Natural Science Foundation for Outstanding Young Scholars of Fujian Province under the grant 2019J06017, in part by the Natural Science Foundation of Fujian Province under the grant 2017J05103, in part by the Fujian-100 Talented People Program, in part by the Key Science and Technology Project of Xiamen City under the grant 3502ZCQ20191005, in part by High-level Talent Innovation Program of Quanzhou City under the grant 2017G027, in part by the Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University under the grants ZQN-YX403 and ZQN-PY418, and in part by the High-Level Talent Project Foundation of Huaqiao University under the grants 14BS201, 14BS204 and 16BS108, and in part by the Subsidized Project for Postgraduates Innovative Fund in Scientific Research of Huaqiao University.

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Correspondence to Huanqiang Zeng.

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Lin, X., Zeng, H., Hou, J. et al. Joint Pyramid Feature Representation Network for Vehicle Re-identification. Mobile Netw Appl 25, 1781–1792 (2020). https://doi.org/10.1007/s11036-020-01561-z

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Keywords

  • Internet of Things
  • Intelligent transport system
  • Vehicle re-identification
  • Joint pyramid feature representation
  • Deep learning