Online vehicle logo recognition using Cauchy prior logistic regression

Ruilong Chen, Matthew Hawes, Olga Isupova, Lyudmila Mihaylova, Hao Zhu

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

9 Citations (SciVal)


Vehicle logo recognition is an important part of vehicle identification in intelligent transportation systems. State-of-the-art vehicle logo recognition approaches typically consider training models on large datasets. However, there might only be a small training dataset to start with and more images can be obtained during the real-time applications. This paper proposes an online image recognition framework which provides solutions for both small and large datasets. Using this recognition framework, models are built efficiently using a weight updating scheme. Another novelty of this work is that the Cauchy prior logistic regression with conjugate gradient descent is proposed to deal with the multinomial classification tasks. The Cauchy prior results in a quicker convergence speed for the weight updating process which could decrease the computational cost for both online and offline methods. By testing with a publicly available dataset, the Cauchy prior logistic regression deceases the classification time by 59%. An accuracy of up to 98.80% is achieved when the proposed framework is applied.

Original languageEnglish
Title of host publication2017 20th International Conference on Information Fusion (Fusion)
Number of pages8
ISBN (Electronic)978-0-9964-5270-0
ISBN (Print)978-1-5090-4582-2
Publication statusPublished - 15 Aug 2017
Event20th International Conference on Information Fusion, Fusion 2017 - Xi'an, China
Duration: 10 Jul 201713 Jul 2017


Conference20th International Conference on Information Fusion, Fusion 2017


  • Cauchy Prior
  • Conjugate Gradient Descent
  • Logistic Regression
  • Online Learning
  • Vehicle Logo Recognition


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