Practical Homography-based perspective correction method for License Plate Recognition

Yang, Shih-Jui; Ho, Chian C.; Chen, Jian-Yuan; Chang, Chuan‐Yu

doi:10.1109/isic.2012.6449740

Cited by 20 publications

(15 citation statements)

References 5 publications

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“…The perspective rectification algorithm is based on the homography, which is a projective transformation [25,41]. With a known homography matrix H, a point p j = (u j , v j , 1) in an image plane can be correspondingly transformed to a point q j = (x j , y j , 1) in another plane, as expressed in Equation (1).…”

Section: Homography-based Perspective Rectificationmentioning

confidence: 99%

Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model

Pham

Kim

et al. 2020

Sensors

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In this study, we investigate a novel idea of using synthetic images of bolts which are generated from a graphical model to train a deep learning model for loosened bolt detection. Firstly, a framework for bolt-loosening detection using image-based deep learning and computer graphics is proposed. Next, the feasibility of the proposed framework is demonstrated through the bolt-loosening monitoring of a lab-scaled bolted joint model. For practicality, the proposed idea is evaluated on the real-scale bolted connections of a historical truss bridge in Danang, Vietnam. The results show that the deep learning model trained by the synthesized images can achieve accurate bolt recognitions and looseness detections. The proposed methodology could help to reduce the time and cost associated with the collection of high-quality training data and further accelerate the applicability of vision-based deep learning models trained on synthetic data in practice.

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Section: Homography-based Perspective Rectificationmentioning

confidence: 99%

Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model

Pham

Kim

et al. 2020

Sensors

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“…In addition, it is assumed that features are consistent for both objects nearer to the camera and farther from the camera. Most of the Optical Character Recognition (OCR) systems and License Plate Recognition (LPR) systems employ perspective correction [77,89]. However, the features available for the farthest object decrease with increase in perspective distance away from the camera [77].…”

Section: Resultsmentioning

confidence: 99%

Estimation of crowd density by clustering motion cues

et al. 2014

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Understanding crowd behavior using automated video analytics is a relevant research problem in recent times due to complex challenges in monitoring large gatherings. From an automated video surveillance perspective, estimation of crowd density in particular regions of the video scene is an indispensable tool in understanding crowd behavior. Crowd density estimation provides the measure of number of people in a given region at a specified time. While most of the existing computer vision methods use supervised training to arrive at density estimates, we propose an approach to estimate crowd density using motion cues and hierarchical clustering. The proposed method incorporates optical flow for motion estimation, contour analysis for crowd silhouette detection, and clustering to derive the crowd density. The proposed approach has been tested on a dataset collected at the Melbourne Cricket Ground (MCG) and two publicly available crowd datasets-Performance Evaluation of Tracking and Surveillance (PETS) 2009 and University of California, San Diego (UCSD) Pedestrian Traffic Database-with different crowd densities (medium-to high-density crowds) and in varied environmental conditions (in the presence of partial occlusions). We show that the proposed approach results in accurate estimates of crowd density. While the maximum mean error of 3.62 was received for MCG and PETS datasets, it was 2.66 for UCSD dataset. The proposed approach delivered superior performance in 50 % of the cases on PETS 2009 dataset when compared with existing methods. Electronic supplementary materialThe online version of this article (

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“…The authors in [28] discover a homography-based perspective correction method for the VLP tilt correction. Their obtained results show that the rate of perspective correction for the VLP database of motorcycle and automotive are 89 % and 97 %, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…Homography-based perspective correction method [28] Horizontal/vertical tilt correction. Tolerant to different lighting, contrast and size of LPs.…”

Section: Correction Contributions Limitationsmentioning

confidence: 99%

Vehicle License Plate Detection and Perspective Rectification

Alhussein¹,

Aurangzeb²,

Haider³

2019

ELEKTRON ELEKTROTECH

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The character segmentation and perspective rectification of Vehicle License Plate (VLP) is essential in different applications, including traffic monitoring, car parking, stolen vehicle recovery, and toll payment. The character segmentation of the VLP and its horizontal as well as vertical (pan and tilt) correction is a crucial operation. It has considerable impact on the precision of the vehicle identification process. In this work, we investigate an effective framework for the perspective rectification and homography correction of vehicle's images. The captured images of the vehicle could be tilted in vertical or horizontal or vertical-horizontal mix directions due to different movements. For reasonable high identification results, a polynomial fitting based homography correction method for rectifying the tilted VLPs is applied. A method for determining four corner points of the rotated VPLs is explored. These four detected corner points are applied in the homography correction algorithm. For comprehensively evaluating the performance of the proposed framework, the detected VLPs in various directions, such as horizontal, vertical, and mix horizontal-vertical, are rotated. For the experiments, the real images of the vehicles in the outdoor environment, from different directions and different distances are captured. With our proposed method, we achieve an accuracy of 97 % and 95 % for the simulated and real captured images, respectively.

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Practical Homography-based perspective correction method for License Plate Recognition

Cited by 20 publications

References 5 publications

Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model

Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model

Estimation of crowd density by clustering motion cues

Vehicle License Plate Detection and Perspective Rectification

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