[논문]Temporal matching prior network for vehicle license plate detection and recognition in videos

Yoo, Seok Bong; Han, Mikyong

doi:10.4218/etrij.2019-0245

[국내논문] Temporal matching prior network for vehicle license plate detection and recognition in videos 원문보기

ETRI journal, v.42 no.3, 2020년, pp.411 - 419

Yoo, Seok Bong (Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute) , Han, Mikyong (Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute)

Abstract ▼ AI-Helper

In real-world intelligent transportation systems, accuracy in vehicle license plate detection and recognition is considered quite critical. Many algorithms have been proposed for still images, but their accuracy on actual videos is not satisfactory. This stems from several problematic conditions in videos, such as vehicle motion blur, variety in viewpoints, outliers, and the lack of publicly available video datasets. In this study, we focus on these challenges and propose a license plate detection and recognition scheme for videos based on a temporal matching prior network. Specifically, to improve the robustness of detection and recognition accuracy in the presence of motion blur and outliers, forward and bidirectional matching priors between consecutive frames are properly combined with layer structures specifically designed for plate detection. We also built our own video dataset for the deep training of the proposed network. During network training, we perform data augmentation based on image rotation to increase robustness regarding the various viewpoints in videos.

Keyword

AI 본문요약
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제안 방법

To address the issues in real-world videos identified across the existing algorithms [1−13], in this study, we designed a CNN architecture with temporal matching priors between adjacent frames.
To effectively reflect the temporal matching priors of FM and BM in a deep CNN, we present a new architecture that is suitable for a video sequence, as illustrated in Figure 2. The architecture consists of two major stages: license plate detection and license plate recognition. In the first stage, we exploit additional information in the previous (t In this study, we proposed a network for the detection and recognition of vehicle license plates by combining the strengths of temporal matching and CNNs.

대상 데이터

) for plate detection and 23 425 plate images for plate recognition. The training sets originated from the Korean vehicle dataset. Based on the preliminary tests described in Section 3.

참고문헌 (17)

Y. Yuan et al., A robust and efficient approach to license plate detection, IEEE Trans. Image Process. 26 (2017), no. 3, 1102-1114.

상세보기
H. Li and C. Shen, Reading car license plates using deep convolutional neural networks and LSTMs, arXiv preprint arXiv:1601.05610, 2016.
S. G. Kim, H. G. Jeon, and H. I. Koo, Deep-learning-based license plate detection method using vehicle region extraction, Electron. Lett. 53 (2017), no. 15, 1034-1036.

상세보기
H. Li, P. Wang, and C. Shen, Toward end-to-end car license plate detection and recognition with deep neural networks, IEEE Trans. Intell. Transp. Syst. 20 (2019), no. 3, 1126-1136.

상세보기
S. M. Silva and C. R. Jung, License plate detection and recognition in unconstrained scenarios, in Proc. Europ. Conf. Comp. Vis., Munich, Germany, 2018, pp. 593-609.
K. S. Raghunandan et al., Riesz fractional based model for enhancing license plate detection and recognition, IEEE Trans. Circuits Syst. Video Technol. 28 (2018), no. 9, 2276-2288.

상세보기
J. Zhuang et al., Towards Human-level license plate recognition, in Proc. Eur. Conf. Comp. Vis., Munich, Germany, 2018, pp. 306-321.
M. A. Rafique, W. Pedrycz, and M. Jeon, Vehicle license plate detection using region-based convolutional neural networks, Soft Comput. 22 (2018), no. 19, 6429-6440.

상세보기
D. Wang et al., LPR-Net: Recognizing Chinese license plate in complex environments, Pattern Recognit. Lett., To be published.
Y. Zhao et al., Chinese license plate image database building methodology for license plate recognition, J. Electron. Imaging 28 (2019), no. 1, 013001.
X. Zhang et al., Vehicle license plate detection and recognition using deep neural networks and generative adversarial networks, J. Electron. Imaging 27 (2018), no. 4, 043056.
K. Khan and M. Choi, Automatic license plate detection and recognition framework to enhance security applications, J. Electron. Imaging 28 (2019), no. 1, 013036.
I. Turkyilmaz and K. Kacan, License plate recognition system using artificial neural networks, ETRI J. 39 (2017), no. 2, 163-172.

원문보기 상세보기
Korean vehicle dataset. Available from https://github.com/seokbongyoo/Dataset_for_LPR [last accessed May 2019].
OpenALPR. Available from https://www.openalpr.com/cloud-api.html [last accessed May 2019].
J. Redmon and A. Farhadi, YOLOv3: An incremental improvement, arXiv preprint arXiv:1804.02767, 2018.
Caltech vehicle dataset. Available from http://www.vision.caltech.edu/archive.html [last accessed May 2019].

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DOI : 10.4218/etrij.2019-0245 [무료]
ScienceCentral : 저널
Korea Open Access Journals : 저널
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