• The KIPS Transactions:PartB
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• v.9B no.1
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• pp.119-128
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• 2002

This paper proposes an efficient recognition method of car license plate from the car images by using both the dynamical thresholding and the neural network with enhanced learning algorithm. The car license plate is extracted by the dynamical thresholding based on the structural features and the density rates. Each characters and numbers from the p]ate is also extracted by the contour tracking algorithm. The enhanced neural network is proposed for recognizing them, which has the algorithm of combining the modified ART1 and the supervised learning method. The proposed method has applied to the real-world car images. The simulation results show that the proposed method has better the extraction rates than the methods with information of the gray brightness and the RGB, respectively. And the proposed method has better recognition performance than the conventional backpropagation neural network.

• Journal of the Korea Society of Computer and Information
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• v.16 no.6
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• pp.71-77
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• 2011

In this paper, we propose a learning based super resolution algorithm using local block for image enhancement of vehicle license plate. Local block is defined as the minimum measure of block size containing the associative information in the image. Proposed method essentially generates appropriate local block sets suitable for various imaging conditions. In particular, local block training set is first constructed as ordered pair between high resolution local block and low resolution local block. We then generate low resolution local block training set of various size and blur conditions for matching to all possible blur condition of vehicle license plates. Finally, we perform association and merging of information to reconstruct into enhanced form of image from training local block sets. Representative experiments demonstrate the effectiveness of the proposed algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.5
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• pp.1004-1012
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• 2007

In this paper, we proposed a new car license plate recognition method using an unsupervised ART2 algorithm with HSI color model. The proposed method consists of two main modules; extracting plate area from a vehicle image and recognizing the characters in the plate after that. To extract plate area, hue(H) component of HSI color model is used, and the sub-area containing characters is acquired using modified fuzzy binarization method. Each character is further divided by a 4-directional edge tracking algorithm. To recognize the separated characters, noise-robust ART2 algorithm is employed. When the proposed algorithm is applied to recognize license plate characters, the extraction rate is better than that of existing RGB model and the overall recognition rate is about 97.4%.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.6
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• pp.427-437
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• 2019

In January, 2019, the Ministry of Science and ICT announced the third edition of Master Plans for Radio Wave Promotion, effective from 2019 to 2023. The focus of this plan is to implement market based radio wave policy and horizontal regulatory framework. Although it appears to be past due, such change in the radio wave policy is not only fair and structured, but also unprecedented and ground breaking in legality. In order to successfully implement market based radio wave policy and horizontal regulatory framework, we must implement identical radio wave license based on license with consideration and time-limited usage. Through this, efficient distribution of finite radio wave resource may come to reality. Furthermore, there must be an effort to include life style regulation on license into radio wave act in the future.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.361-368
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• 2014

The LPR(License plate recognition) system has been developed to efficient control for complex traffic environment and currently be used in many places. However, because of light, noise, background changes, environmental changes, damaged plate, it only works limited environment, so it is difficult to use in real-time. This paper presents a heuristic segmentation algorithm for robust to noise and illumination changes and introduce a real-time license plate recognition system using it. In first step, We detect the plate utilized Haar-like feature and Adaboost. This method is possible to rapid detection used integral image and cascade structure. Second step, we determine the type of license plate with adaptive histogram equalization, bilateral filtering for denoise and segment accurate character based on adaptive threshold, pixel projection and associated with the prior knowledge. The last step is character recognition that used histogram of oriented gradients (HOG) and multi-layer perceptron(MLP) for number recognition and support vector machine(SVM) for number and Korean character classifier respectively. The experimental results show license plate detection rate of 94.29%, license plate false alarm rate of 2.94%. In character segmentation method, character hit rate is 97.23% and character false alarm rate is 1.37%. And in character recognition, the average character recognition rate is 98.38%. Total average running time in our proposed method is 140ms. It is possible to be real-time system with efficiency and robustness.

• Journal of Information Processing Systems
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• v.6 no.2
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• pp.253-260
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• 2010

Communication devices aim to provide a multimedia service without spatial or temporal limitations in an IP-based environment. However, it is incapable of allowing for fair use by consumers who legally buy content, and damages provider contents through the indiscriminate distribution and use of illegal contents. The DRM system that emerged to solve this problem cannot protect licenses stored on communication devices, and manage licenses by redistribution. This paper proposes a license audit model, which checks for illegal access, modification and redistribution, and reports alert logs to the server.

• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.103-113
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• 2019

License plate recognition camera is dedicated device designed for acquiring images of the target vehicle for recognizing letters and numbers in a license plate. Mostly, it is used as a part of the system combined with server and image analysis module rather than as a single use. However, building a system for vehicle license plate recognition is costly because it is required to construct a facility with a server providing the management and analysis of the captured images and an image analysis module providing the extraction of numbers and characters and recognition of the vehicle's plate. In this study, we would like to develop an embedded type convergent camera (Edge Base) which can expand the function of the camera to not only the license plate recognition but also the security CCTV function together and to perform two functions within the camera. This embedded type convergence camera equipped with a high resolution 4K IP camera for clear image acquisition and fast data transmission extracted license plate area by applying YOLO, a deep learning software for multi object recognition based on open source neural network algorithm and detected number and characters of the plate and verified the detection accuracy and recognition accuracy and confirmed that this camera can perform CCTV security function and vehicle number plate recognition function successfully.

• Journal of Practical Engineering Education
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• v.5 no.2
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• pp.169-176
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• 2013

Recently the construction of simulation environment is an important issue in all fields. In case of the training for operating machines such as airplanes or spaceships which cause a huge cost, simulators could be helpful to reduce the costs and training efforts by simulating real situations. When people get a driver's license, too many trainees have to wait for their turns because of the limited number of cars and the small space of training sites. To solve this problem, we have designed and developed the basic design for the simulators. We suggest the Computer 3D Simulation Model for a driver's practice. The concept of this simulator is from a 3D Racing-game which suits for a driving exercise. We provide users with handle-controlled simulation settings to let users feel reality as if they drive in real through this simulator. We also use a 'force-feedback' system which gives handle vibration when users collide against obstacles or exceed lanes. Users can be absorbed in the simulation program and feel the sense of the real. This paper is the study about modeling the driving exercise model of 'computer 3D simulation', and producing and utilizing the simulator through this modeling.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2645-2650
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• 2014

This paper researches on securing parking spaces and improving parking performance, by use of vehicle license plate investigation for average parking time and parking turnover ratio. Even though general surveys on vehicle license plate investigation are performed by the person, our survey is performed by the machine, CCTV. Unusually, our method checks average parking time and parking turnover ratio at time intervals of the survey. So, it is easy to check whether a parking lot is effectively used and its traffic flows smoothly or not. In our experiment, we exclude a method to recognize characters by use of 4-direction projection.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.52
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• pp.33-41
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• 1999

More than 90 percent of traffic accidents were caused by human errors in driving situation. However, the ergonomic studies were not enough to prevent these human errors. Especially, drivers acquire more than 70 percent of information using the vision, and this shows the importance of consideration about the driver's visual cognition characteristics. Consequently, the driver's cognition time and the cognition accuracy were measured for the Instrument Panel(IP) of vehicle cockpit in this study. For the experiment, subjects were divided in two groups. The first group of subjects had their own vehicles and the driver license, and the second group of the subjects didn't have own vehicle but had the driver license. IP Images were used in the experiment and the each image was made by changing layout of objects within devices. The results showed that there were no differences for the cognition time and the accuracy between images within the group, and the results between the groups showed the same trend.