A Local Rotation Transformation Model for Vehicle Re-Identification

Yanbing Chen, Wei Ke, Hao Sheng, Zhang Xiong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The vehicle re-identification (V-ReID) task is critical in urban surveillance and can be used for a variety of purposes. We propose a novel augmentation method to improve the V-ReID performance. Our deep learning framework mainly consists of a local rotation transformation and a target selection module. In particular, we begin by using a random selection method to locate a local region of interest in an image sample. Then, a parameter generator network is in charge of generating parameters for further image rotation transformation. Finally, a target selection module is used to retrieve the augmented image sample and update the parameter generator network. Our method is effective on VeRi-776 and VehicleID datasets, it shows that we achieve considerable competitive results with the current state-of-the-art.

Original languageEnglish
Title of host publicationWireless Algorithms, Systems, and Applications - 17th International Conference, WASA 2022, Proceedings
EditorsLei Wang, Michael Segal, Jenhui Chen, Tie Qiu
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783031192074
Publication statusPublished - 2022
Event17th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2022 - Dalian, China
Duration: 24 Nov 202226 Nov 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13471 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2022


  • Local region
  • Local rotation transformation
  • Parameter generator network
  • Target selection
  • Vehicle re-identification


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