3,822 research outputs found
Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-Resolution
Although most current license plate (LP) recognition applications have been
significantly advanced, they are still limited to ideal environments where
training data are carefully annotated with constrained scenes. In this paper,
we propose a novel license plate recognition method to handle unconstrained
real world traffic scenes. To overcome these difficulties, we use adversarial
super-resolution (SR), and one-stage character segmentation and recognition.
Combined with a deep convolutional network based on VGG-net, our method
provides simple but reasonable training procedure. Moreover, we introduce
GIST-LP, a challenging LP dataset where image samples are effectively collected
from unconstrained surveillance scenes. Experimental results on AOLP and
GIST-LP dataset illustrate that our method, without any scene-specific
adaptation, outperforms current LP recognition approaches in accuracy and
provides visual enhancement in our SR results that are easier to understand
than original data.Comment: Accepted at VISAPP, 201
Automated License Plate Recognition using Existing University Infrastructure and Different Camera Angles
Number or license plate recognition has become an essential technology for traffic and security applications. Providing access control at any organization or academic institution improves the level of security. However, providing security personnel to manually control the access of vehicles at an academic institution is costly, time-consuming, and to a limited extent, error prone. This study investigated the use of an automated vehicle tracking system, incorporating experimental computer vision techniques for license plate recognition that runs in real-time to provide access control for vehicles and provide increased security for an academic institution. A vehicle monitoring framework was designed by using various technologies and experimenting with different camera angles. In addition, the effect of environmental changes on the accuracy of the optical character recognition application was assessed. The Design Science Research methodology was followed to develop the vehicle monitoring framework artifact. Image enhancement algorithms were tested, and the most viable options were evaluated and implemented. Optimal operating criteria that were established for the vehicle monitoring framework achieved a 96% success rate. The results indicate that a cost-effective solution could be provided by using an existing camera infrastructure at an academic institution and suitable license plate recognition software technologies, algorithms, and different camera angles
Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers
Recent years have seen significant developments in the field of License Plate
Recognition (LPR) through the integration of deep learning techniques and the
increasing availability of training data. Nevertheless, reconstructing license
plates (LPs) from low-resolution (LR) surveillance footage remains challenging.
To address this issue, we introduce a Single-Image Super-Resolution (SISR)
approach that integrates attention and transformer modules to enhance the
detection of structural and textural features in LR images. Our approach
incorporates sub-pixel convolution layers (also known as PixelShuffle) and a
loss function that uses an Optical Character Recognition (OCR) model for
feature extraction. We trained the proposed architecture on synthetic images
created by applying heavy Gaussian noise to high-resolution LP images from two
public datasets, followed by bicubic downsampling. As a result, the generated
images have a Structural Similarity Index Measure (SSIM) of less than 0.10. Our
results show that our approach for reconstructing these low-resolution
synthesized images outperforms existing ones in both quantitative and
qualitative measures. Our code is publicly available at
https://github.com/valfride/lpr-rsr-ext
License Plate Super-Resolution Using Diffusion Models
In surveillance, accurately recognizing license plates is hindered by their
often low quality and small dimensions, compromising recognition precision.
Despite advancements in AI-based image super-resolution, methods like
Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs)
still fall short in enhancing license plate images. This study leverages the
cutting-edge diffusion model, which has consistently outperformed other deep
learning techniques in image restoration. By training this model using a
curated dataset of Saudi license plates, both in low and high resolutions, we
discovered the diffusion model's superior efficacy. The method achieves a
12.55\% and 37.32% improvement in Peak Signal-to-Noise Ratio (PSNR) over SwinIR
and ESRGAN, respectively. Moreover, our method surpasses these techniques in
terms of Structural Similarity Index (SSIM), registering a 4.89% and 17.66%
improvement over SwinIR and ESRGAN, respectively. Furthermore, 92% of human
evaluators preferred our images over those from other algorithms. In essence,
this research presents a pioneering solution for license plate
super-resolution, with tangible potential for surveillance systems
Recent Trends and Techniques in Text Detection and Text Localization in a Natural Scene: A Survey
Text information extraction from natural scene images is a rising area of research. Since text in natural scene images generally carries valuable details, detecting and recognizing scene text has been deemed essential for a variety of advanced computer vision applications. There has been a lot of effort put into extracting text regions from scene text images in an effective and reliable manner. As most text recognition applications have high demand of robust algorithms for detecting and localizing texts from a given scene text image, so the researchers mainly focus on the two important stages text detection and text localization. This paper provides a review of various techniques of text detection and text localization
Incorporating negentropy in saliency-based search free car number plate localization
License plate localization algorithms aim to detect license plates within the scene. In this paper, a new algorithm is discussed where the necessary conditions are imposed into the saliency detection equations. Measures of distance between probability distributions such as negentropy finds the candidate license plates in the image and the Bayesian methodology exploits the a priori information to estimate the highest probability for each candidate. The proposed algorithm has been tested for three datasets, consisting of gray-scale and color images. A detection accuracy of 96% and an average execution time of 80 ms for the first dataset are the marked outcomes. The proposed method outperforms most of the state-of-the-art techniques and it is suitable to use in real-time ALPR applications
- …