License Plate Recognition Algorithm Research Articles

Trusted forensics scheme based on digital watermark algorithm in intelligent VANET

Trusted forensics is one of the most important problems in VANET, and it often needs continuous video monitoring, once break out emergent vehicle accidents, then specific staff members take steps for forensics to obtain facts and define responsibility. Traditional forensics exists problems of inaccurate information, unfair responsibility definition and risk of leakage of user’s privacy. To solve the above problem, in this paper, we proposed a trusted forensics scheme based on digital image watermark in intelligent VANET, in which we proposed technical and fair algorithms for trusted forensics, and the trusted forensics scheme includes basic forensics parameter data obtaining critical forensics data automatic generation and forensics data extraction. Once there vehicle accident occurred, the forensics system first obtains the location, timestamp, forensics device data as basic forensics parameter data, and then, it embeds the forensics parameter as watermark into the real-time vehicle accident photograph by the proposed digital watermark algorithm, and thus, the real-time and undeniable forensics data are automatic generated as evidence; when necessary, the forensics system can extract the evidence data and watermark data from the critical forensics data. The proposed scheme can detect the content integrity of image data and even find out tampering mark when the image data are tampered. Additionally, we used neural network algorithm for vehicle license plate recognition and rapid vehicle information gathering. Finally, experiments evaluations manifest the proposed is forensics scheme is secure, robust, and efficient in vehicle forensics.

Vision-based nonintrusive context documentation for earthmoving productivity simulation

Abstract Although video surveillance systems have shown potential for analyzing jobsite contexts, the necessity of a complex multi-camera surveillance system or workers' privacy issues remain as substantive hurdles to adopt such systems in practice. To address such issues, this study presents a non-intrusive earthmoving productivity analysis method using imaging and simulation. The site access log of dump trucks is used to infer earthmoving contexts, which is produced by analyzing videos recorded at the entrance and the exit of a construction site. An algorithm for license plate detection and recognition in an uncontrolled environment is developed to automatically produce the site access log, by leveraging video deinterlacing, a deep convolutional network, and rule-based post-processing. The experimental results show the effectiveness of the proposed method for producing the site access log. Based on the site access log, simulation-based productivity analysis is conducted to produce a daily productivity report, which can provide the basis for earthmoving resource planning. It is expected that the resulting daily productivity report promotes data-driven decision-making for earthmoving resource allocation, thereby improving potential for saving cost and time for earthworks with an updated resource allocation plan.

Lithuanian traffic monitoring system (LTTMS) based on Android

The number of vehicles is growing exponentially all over the world. Therefore, spreading a real-time information to commuters is necessary. Recently, the GPS availability on smartphones and abundance of license plate recognition algorithms made a mobile phone-based traffic monitoring system highly convenient. In this paper we will introduce a transportation monitoring system for Lithuania. A country which is lacking a reliable traffic application capable of generating traffic data, informing users about the situation on the road and helping to prevent crimes.

Complex environment image recognition algorithm based on GANs and transfer learning

With the rapid development of the global economy, people’s living standards have gradually improved, and the way of transportation has undergone earth-shaking changes. This has created a series of social and environmental issues while greatly facilitating people’s lives and work. Excessive numbers of cars have caused urban traffic problems such as traffic pollution, traffic jams and traffic accidents. The strong economic strength has made the automobile industry appear in a booming stage and made it enter the automobile society earlier. The license plate is a vehicle-passed ID card, so the detection and identification of the license plate has become one of the important research directions in today’s society. Although the resolution of today’s surveillance equipment is getting higher, there are still quite a few monitors with low resolution. Conventional license plate detection and recognition algorithms are challenged in low-resolution video processing. In this paper, we introduce the theory and methodology of GAN and transfer learning, which are applied to deal with the license plate image recognition under several complex environments. The experimental results show that the method adopted in this paper has higher recognition rate and robustness.

A GPU-Accelerated LPR Algorithm on Broad Vision Survillance Cameras

Video surveillance systems are employed to prevent crime, mounting hundreds of cameras and sensors monitoring activities during the whole day. Due to the huge amount of video information generated in real time, these surveillance centers are requiring more technology and intelligence to support human operators in many complex situations. There are important analyses that could be realized with this video-data: from criminalistics event detection to particular object recognition. One important tool is License Plate Recognition (LPR) that helps detecting vehicles that could have been robbed. Although corporative solutions exist, these techniques require a lot of processing power and special located cameras, that not always could be afford by the local government. In this context, the proposed project is based on applying open-source LPR algorithms that runs on already existent surveillance cameras. These cameras are observing a complete scene (not just a line as it is commonly used), so LPR algorithms are rather slow, processing only 1 image per second. For this reason, the objective is to improve the performance combining a parallel LPR running on graphic processor units (GPU) and object tracking algorithms. This work describes the ongoing implementation, the techniques currently used for object tracking and LPR implementation, and exposes results regarding the efficiency of the solution.

Bees algorithm and support vector machine for Malaysian license plate recognition

Nowadays the increasing use of vehicles in modern life raises the problem of designing techniques that support effective traffic monitoring and vehicle identification. License plate recognition is an advanced machine vision technology used to identify vehicles by their license plates without direct human intervention. License plate recognition system major steps include image capturing, preprocessing, segmentation, feature extraction and classification. In this paper, a complete system for Malaysian license plate recognition is proposed to handle special Malaysian license plates under different conditions. In the proposed system, peak signal to noise ratio (PSNR) and bees algorithm is used in preprocessing and segmentation stages, Sobel method for edge detection, and support vector machine for classification. The proposed system is applied to dataset that is consisted of 1,216 grey scale images, and is compared with different methods in segmentation and classification. Results show the robustness of the proposed system, making them suitable for more real world applications.

Automatic Fuzzy License Plate Recognition Based on Deep Learning

At present, the clear license plate recognition algorithm has been mature, but for the blurred license plate which cannot be recognized by the human eye, the recognition rate of the traditional license plate recognition algorithm is too low. In view of this, a license plate character recognition algorithm based on convolutional neural network structure is proposed. In this study, a large number of training samples of fuzzy characters are constructed, and a convolutional neural network is trained for blind segmentation of fuzzy license plate characters. By calling the trained convolutional neural network, we can recognize the characters after blind segmentation. The experimental results show that the training set accuracy is close to 100%, and the accuracy of the test set is more than 93%. It can recognize the license plate characters which cannot be recognized by the human eye.

EMPIRICAL STUDY OF CAR LICENSE PLATES RECOGNITION

The number of vehicles on the road has increased drastically in recent years. The license plate is an identity card for a vehicle. It can map to the owner and further information about vehicle. License plate information is useful to help traffic management systems. For example, traffic management systems can check for vehicles moving at speeds not permitted by law and can also be installed in parking areas to se-cure the entrance or exit way for vehicles. License plate recognition algorithms have been proposed by many researchers. License plate recognition requires license plate detection, segmentation, and charac-ters recognition. The algorithm detects the position of a license plate and extracts the characters. Various license plate recognition algorithms have been implemented, and each algorithm has its strengths and weaknesses. In this research, I implement three algorithms for detecting license plates, three algorithms for segmenting license plates, and two algorithms for recognizing license plate characters. I evaluate each of these algorithms on the same two datasets, one from Greece and one from Thailand. For detecting li-cense plates, the best result is obtained by a Haar cascade algorithm. After the best result of license plate detection is obtained, for the segmentation part a Laplacian based method has the highest accuracy. Last, the license plate recognition experiment shows that a neural network has better accuracy than other algo-rithm. I summarize and analyze the overall performance of each method for comparison.

License plate recognition algorithm on the basis of a connected components method and a hierarchical temporal memory model

This paper proposes a license plate recognition algorithm that consists of three major steps: image preprocessing, segmentation, and recognition, which works efficiently with day- and nighttime images, as well as with the license plate being tilted. Pre-filtration allows the sequential binarization to be conducted efficiently. Typically, the license plate segmentation is realized by a histogram method with the preliminary plate de-rotation to the horizontal position, thus deteriorating the original image quality. In this paper the segmentation is implemented by a connected components method, enabling the rotation and a consequent loss of quality to be avoided. The hierarchical temporal network shows good results in rotated symbols recognition. The proposed method can be used in a similar way for segmentation and recognition of various text data. The proposed algorithms can also be used for distorted text segmentation and recognition.

안드로이드 기반 스마트 캠 방식의 저가형 자동차 번호판 인식 시스템 구현에 관한 연구

본 논문에서는 안드로이드 운영체제를 기반으로 하는 스마트 캠 방식의 저가형 자동차 번호판 인식 시스템을 제안한다. 제안하는 시스템은 휴대용 단말기와 서버로 구성된다. 단말기 하드웨어부는 ARM Cortex-A9(S5PV210) 프로세서로 이루어진 제어부, 전원부, 유무선통신부, 입출력부 등으로 구성된다. 단말기에 사용되는 카메라와 WiFi 모듈을 위한 리눅스 커널을 포팅하고 전용 디바이스 드라이버를 개발하였다. 번호판 인식 알고리즘은 캐니 에지검출기를 사용한 번호판 후보영역 설정, 레이블링을 이용한 번호판 번호 추출, 템플릿 매칭을 이용한 번호인식 등으로 구현된다. 단말기에 의하여 인식된 번호는 사용자가 소지한 휴대폰을 통하여 원격지의 서버에 전송되어 차량상태를 데이터베이스에서 검색하여 다시 단말기로 전송 해주게 된다. 본 논문에서 제안하는 시스템을 효용성을 입증하기 위하여 자연환경에서 사용자가 직접 단말기를 휴대하고 임의의 차량 번호판을 촬영하여 인식률을 확인한 결과, 95%의 인식률을 보였다. 제안된 시스템은 저가형의 휴대용 번호판 인식기에 적합하며, 안드로이드 운영체제를 사용함으로써 장기간 사용 시에도 시스템의 안정성을 가능케 하였다. In this paper, we propose a low-cost license plate recognition system based on smart cam system using Android. The proposed system consists of a portable device and server. Potable device Hardware consists of ARM Cortex-A9 (S5PV210) processor control unit, a power supply device, wired and wireless communication, input/output unit. We develope Linux kernel and dedicated device driver for WiFi module and camera. The license plate recognition algorithm is consisted of setting candidate plates areas with canny edge detector, extracting license plate number with Labeling, recognizing with template matching, etc. The number that is recognized by the device is transmitted to the remote server via the user mobile phone, and the server re-transfer the vehicle information in the database to the portable device. To verify the utility of the proposed system, user photographs the license plate of any vehicle in the natural environment. Confirming the recognition result, the recognition rate was 95%. The proposed system was suitable for low cost portable license plate recognition device, it enabled the stability of the system when used long time by using the Android operating system.

Survey on Various Types of License Plate Localization, Segmentation and Recognition Algorithms

Survey on Various Types of License Plate Localization, Segmentation and Recognition Algorithms

Research on License Plate Recognition Algorithm based on Support Vector Machine

Support Vector Machine (SVM) as an important theory of machine learning and patternrecognition has been well applied to small sample clustering learning, nonlinear problems, outlierdetection and so on. The license plate automation recognition system has received extensiveattention as an important application of machine learning and pattern recognition in intelligent transport.The license plate recognition system is composed of three parts, license plate preprocessing and location, license plate character segment, license plate character recognition. In this paper, we mainly introduce the flow of license plate recognition, related technology and support vector machinetheory. Experimental results show the effectiveness of our method.

License Plate Recognition: A Brief Tutorial

With the rapid development of public transportation system, automatic identification of vehicles become more and more practical in many applications during the past two decades. Nowadays, Intelligent Transportation Systems (ITS) are having a wide impact in people?s life as their scope is to improve transportation safety and mobility and to enhance productivity through the use of advanced technologies. ITS are divided into intelligent infrastructure systems and intelligent vehicle systems. A computer vision and character recognition algorithm for license plate recognition can be used as a core for intelligent infrastructure like electronic payment systems (toll payment, parking fee payment), freeway and arterial management systems for traffic surveillance. Moreover, as increased security awareness has made the need for vehicle based authentication technologies extremely significant the proposed system may be employed as access control system for monitoring of unauthorized vehicles entering private areas.

Research and Implementation of a License Plate Recognition Algorithm Based on Hierarchical Classification

This paper proposed an improved method for license plate recognition based on hierarchical classification. First, the method of feature extraction and dimension reduction is presented by finding the optimal wavelet packet basis in the process of wavelet packet decomposition and K-L transform. Then the recognition algorithm is introduced based on feature extraction and hierarchical classification. Finally, the principles and procedures of using support vector machines, Harris corner detection algorithm and digital character classification are explained in detail. Simulation results indicate that the presented recognition algorithm performs well with higher speed and efficiency in recognition.

Saudi License Plate Recognition Algorithm Based on Support Vector Machine

License plate recognition (LPR) is an image processing technology that is used to identify vehicles by their license plates. This paper presents a license plate recognition algorithm for Saudi car plates based on the support vector machine (SVM) algorithm. The new algorithm is efficient in recognizing the vehicles from the Arabic part of the plate. The performance of the system has been investigated and analyzed. The recognition accuracy of the algorithm is about 93.3%.

License Plate Recognition Based on Neural Network Algorithm to Improve Research

with the development of intelligent transportation technology, which all countries are suitable for their own license plate recognition system is developed. But because of the CCD camera Angle problem will make license plate image tilt; Segmentation after do not match the characters in size and character discontinuity, led to license plate recognition rate is not high, speed slow, reduce the real-time performance of the system. In order to improve the rate of convergence, the recognition rate presents a license plate recognition algorithm based on BP neural network. First put the image correction, segmentation of character normalization processing and eliminate the unfavorable factors, then puts forward characteristics of characters input for training the BP neural network. By setting the network weights and training transfer function, improved algorithm to improve the recognition rate of the system, as well as the real-time performance.

저 조도 영상에서의 말레이시아 차량 번호판 인식

말레이시아 차량 번호판에는 플라스틱으로 제작된 영문 및 숫자들이 엠보싱 형태로 부착되어있으며 수평 수직방향 문자들 사이 간격이 조밀하게 배치된 경우가 많다. 따라서 조도가 낮은 차량 영상에서는 번호판 문자 획 정보 추출이 어려워질 수 있다. 본 논문에서는 저 조도에서 촬영된 말레이시아 차량 영상에서 번호판을 인식하는 알고리즘을 제안하였다. 저 조도에서 촬영된 차량 영상에서도 문자 획 연결 요소를 정확하게 추출하기 위해 DoG 필터링 기반 문자 획 생성 기법을 도입하였다. 문자 획 연결요소 해석을 통한 번호판 후보 영역을 추정한 다음 문자 영역을 분할하고 인식을 하였다. 쿠알라룸푸르 도로상에서 조명이 부착되지 않은 IR 카메라를 사용하여 주야로 촬영한 6,046장의 차량 영상을 대상으로 번호판 인식 실험을 수행하였다. 제안된 알고리즘을 이용하여 실험해 본 결과 번호판 인식 성능이 96.1%로 나타났다. In the Malaysian license plates, alphabets and numerals which are made by plastic, are adhered to a frame as embossing style and occasionally characters in horizontal, vertical directions are aligned with narrow space. So the extraction of character stroke information can be hard in the vehicle images of low illumination intensity. In this paper, Malaysian license plate recognition algorithm for low illumination intensity image is proposed. DoG filtering based character stroke generation method is introduced to derive exact connected components of strokes in the vehicle image of low illumination intensity. After localization of plate by connected component analysis, characters are segmented and recognized. Algorithm is experimented for the 6,046 vehicle images captured in Kuala Lumpur by IR camera without using any special light during day and night. The experimental results show that recognition accuracy of plates is 96.1%.

Efficiency of the Electronic License Plate Recognition Systems

The precise recognition of the vehicle determines the efficiency of intelligent transportation systems. Intelligent transportation systems become more effective if it can identify the vehicle. Electronic license plate recognition systems used to solve this problem. Proposed electronic license plate recognition systems model adapted for simple internet protocol video surveillance cameras. An analysis of the internet protocol video surveillance camera‘s processors, operating principle and software created guidelines for the algorithm. According to performance of the internet protocol video surveillance camera formed four efficiency criteria for license plate recognition algorithms. It's performed the experiment with four algorithms to search for a license number plate by different attributes of the license number. The most efficient algorithm was selected by criteria from experimental results. The algorithm is further analyzed and proposed the application model of the internet protocol video surveillance camera. DOI: http://dx.doi.org/10.5755/j01.eee.19.8.5396

An Algorithm of License Plate Recognition Based on Improved PSO-BP Network

Aiming at the problems of BP network algorithm easily falling into local minimum point, slow converging and the problem that generalization ability can not be guaranteed, a method to improve the PSO is proposed. This method of improved PSO can strengthen the parameters of BP network. Based on this, a license plate recognition algorithm is designed. Some conclusions can be drawn from the experiments: (1) the improved PSO-BP network is stable and robust which can avoid falling into flat areas and local minimum point. (2) the performance and efficiency of license plate recognition based on the improved PSO-BP network is pretty good.

A License Plate Recognition Method Based on Wavelet Transform and Symmetrical Principal Component Analysis Algorithm

In the past, the license plate recognition algorithm has some shortcomings, such as low recognition rate, slow speed of recognition, inaccurate license plate positioning. This paper proposes a new license plate location algorithm based on wavelet transform and the principal component analysis algorithm is used to feature extraction.The experimental results show that this method can reduce the amount of computation and improve the system recognition rate.