Loading...

Real-Time scale-invariant license plate detection using cascade classifiers

Yousefi, E ; Sharif University of Technology | 2019

496 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/MIPR.2019.00081
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2019
  4. Abstract:
  5. This paper presents an online scale-invariant license plate detection (LPD) system with high accuracy for the automatic license plate recognition (ALPR) systems. A dataset of Persian plates is accumulated with more than 44,000 images of plates and 9000 frames of real world roads. For the plate detection and localization, a multi-stage classifier is trained with local binary pattern (LBP) features and a multi-scale algorithm to detect plates with any size within a frame. Besides, we proposed multiple algorithms to boost the performance and accuracy of our solution, including two-stage detection, background subtraction for non-moving areas elimination, and a sophisticated method for estimating the size of the plates in each part of the frames based on linear regression. In our most precise method we achieved 99.6% of accuracy, with detection rate of 83.68 fps. We also proposed several combinations of our algorithms to speeding up the process to 100 fps
  6. Keywords:
  7. Cascade classifier ; Local binary pattern ; Automatic vehicle identification ; License plates (automobile) ; Linear regression ; Online systems ; Optical character recognition ; Automatic license plate recognition ; Background subtraction ; Cascade classifiers ; License plate detection ; Local binary patterns ; Multiple algorithms ; Plate detections ; Two-stage detections ; Classification (of information)
  8. Source: 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019, 28 March 2019 through 30 March 2019 ; Pages 399-402 , 2019 ; 9781728111988 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/8695390