Joint Semi-supervised Learning and Re-ranking for Vehicle Re-identification

Fangyu Wu*, Shiyang Yan, Jeremy S. Smith, Bailing Zhang

*Corresponding author for this work

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

20 Citations (Scopus)


Vehicle re-identification (re-ID) remains an unproblematic problem due to the complicated variations in vehicle appearances from multiple camera views. Most existing algorithms for solving this problem are developed in the fully-supervised setting, requiring access to a large number of labeled training data. However, it is impractical to expect large quantities of labeled data because the high cost of data annotation. Besides, re-ranking is a significant way to improve its performance when considering vehicle re-ID as a retrieval process. Yet limited effort has been devoted to the research of re-ranking in the vehicle re-ID. To address these problems, in this paper, we propose a semi-supervised learning system based on the Convolutional Neural Network (CNN) and re-ranking strategy for Vehicle re-ID. Specifically, we adopt the structure of Generative Adversarial Network (GAN) to obtain more vehicle images and enrich the training set, then a uniform label distribution will be assigned to the unlabeled samples according to the Label Smoothing Regularization for Outliers (LSRO), which regularizes the supervised learning model and improves the performance of re-ID. To optimize the re-ID results, an improved re-ranking method is exploited to optimize the initial rank list. Experimental results on publically available datasets, VeRi-776 and VehicleID, demonstrate that the method significantly outperforms the state-of-the-art.

Original languageEnglish
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538637883
Publication statusPublished - 26 Nov 2018
Event24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
Duration: 20 Aug 201824 Aug 2018

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference24th International Conference on Pattern Recognition, ICPR 2018


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