IQ-STAN: Image Quality Guided Spatio-Temporal Attention Network for License Plate Recognition

Cong Zhang, Qi Wang, Xuelong Li

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

6 Scopus citations

Abstract

License plate recognition (LPR) is one of the essential components in intelligent transportation systems. Although the image processing algorithms for LPR have been extensively studied in the past several years, the recognition performance is still not satisfactory especially in unconstrained complex scenes. In order to tackle this issue, a novel deep multi-task learning-based method is proposed in this paper by introducing contextual information in multiple license plate frames. Specifically, an end-to-end trainable multi-task architecture, namely IQ-STAN, is developed by joint license plate recognition and image quality scoring. Moreover, we propose an image quality-guided spatio-temporal attention mechanism, which is utilized in the frame-level feature representation during the phase of plate recognition. Extensive experiments are conducted and the competitive results demonstrate the effectiveness of our proposed framework.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2268-2272
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: 4 May 20208 May 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Country/TerritorySpain
CityBarcelona
Period4/05/208/05/20

Keywords

  • Image Quality
  • License Plate Recognition
  • Multi-Frame
  • Multi-Task
  • Spatio-Temporal Attention

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