• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.4
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• pp.85-94
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• 2021

The LPR system's trigger sensor makes problem occasionally due to the heave weight of vehicle or the obsolescence equipment. If we replace the hardware sensor to the deep-learning based software sensor in order to generate the trigger signal, LPR system maintenance would be a lot easier. In this paper we proposed the deep-learning sliding window based object detection and tracking algorithm for the LPR system's trigger signal generation. The gate passing vehicle's license plate recognition results are combined into the normal tracking algorithm to catch the position of the vehicle on the trigger line. The experimental results show that the deep learning sliding window based trigger signal generating performance was 100% for the gate passing vehicles including the 5.5% trigger signal position errors due to the minimum bounding box location errors in the vehicle detection process.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.437-440
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• 2012

In this paper, we developed the landfill bringing management system which can help to manage a landfill efficiently. Basically We made efforts to standardize it's system architecture but also considered it's localization to adapt An-sung Landfill's special requirements. We used RFID and LPR systems to distinguish a garbage truck from the others, and could establish more reliable landfill management system with this information.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.161-166
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• 2012

In this paper, we constructed the systematic management and standardization of computerized landfill bringing system in Ansung landfill facility. During the system design process and database construction, we considered the specific items in Ansung landfill facility. And, we systemized and conventionalized the basic core data so as to we apply to another landfill facilities. And, we tried to standardize the system using the exception logic for specific items. For this purpose, we developed the landfill bringing management system which can help to manage a landfill efficiently. Basically We made efforts to standardize it's system architecture but also we considered it's localization to adapt An-sung Landfill's special requirements. We used RFID and LPR systems to distinguish a garbage truck from the others, and could establish more reliable landfill management system with this information. We expect that we can perform the systematic and the efficient landfill Bringing using the automated process of bringing in and taking out the waste process based on the developed system by automated RFID/LPR system.

• Journal of Korea Multimedia Society
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• v.18 no.10
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• pp.1172-1179
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• 2015

A wireless license plate region extracting module is proposed for LPR system controlling multiple gates. This module is cheaply implemented using Raspberry Pi which is open source and high performance. First, as the upper 1/3 of the captured image is discarded as it has no useful information on license plate. Using the OpenCV libraries the edge image is got by Canny algorithm after applying Gaussian filtering to gray image, and the labeling is conducted for 4 consecutive numbers in license plate. These numbers are located using various decision equations, and expanding the numbers region the final license plate region can be extracted. The result image is transferred to Server using wifi direct. Using the proposed module it becomes easy to set up and maintain the LPR system. The experimental results showed that the successful extracting rate was 98.4% using 500 car images with 640 × 480 resolution.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.4
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• pp.19-28
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• 2020

Vehicle license plate recognition system is not generalized in Malaysia due to the loose character layout rule and the varying number of characters as well as the mixed capital English characters and italic English words. Because the italic English word is hard to segmentation, a separate method is required to recognize in Malaysian license plate. In this paper, we propose a mixed character level and word level English license plate recognition algorithm using deep learning neural networks. The difference of Gaussian method is used to segment character and word by generating a black and white image with emphasized character strokes and separated touching characters. The proposed deep learning neural networks are implemented on the LPR system at the gate of a building in Kuala-Lumpur for the collection of database and the evaluation of algorithm performance. The evaluation results show that the proposed Malaysian English LPR can be used in commercial market with 98.01% accuracy.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.2
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• pp.143-148
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• 2014

Particle filter algorithm has been proven very successful for non-linear and non-Gaussian estimation problem and thus it has been widely used for object tracking for video signals. If the object moves significantly, particle filter needs very large number of particles to track object and this results high computational cost. In this paper, modified particle filter by adopting motion vector is proposed for tracking vehicle in low frame rate(LPR) video input, which the object moving significantly and randomly between consecutive frames. In the proposed algorithm, motion vector is applied in selection and observe step. The experimental result shows that the proposed particle filter can track vehicle successfully in the case when previous one fails. And it also shows the propose method increases the precision of tracking.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.173-179
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• 2018

With the recent increase in the number of cars and the lack of parking spaces, the number of parking businesses has increased. the parking management has become an essential element in parking business, and the parking management's company becomes an increasingly popular opportunity. However, as competition grows as more and more and companies increase in number, efforts are being made to create new services, gain technological excellence, or reduce costs through current system improvements. In this paper, we developed a parking management system using cloud based LPR(License Plate Recognition) service for effective parking. Structural improvements in the proposed system reduce costs, simplify installation, and respond quickly to failures and updates.

• Journal of the Korea Organic Resources Recycling Association
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• v.24 no.4
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• pp.39-48
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• 2016

An information management system for a solid waste landfill site was developed, in this study, to optimize the operation and management of solid waste landfill in real time in addition to provide the information of landfill status to the landfill operator, public official concerned and local residents. The landfill information management system is composed of two systems (Solid waste landfill history management system and landfill operation and performance management system). The solid waste landfill history management system based on automated RFID/LPR system allows landfill operators to provide information of waste collection vehicles and received waste. In addition, the system aids in the identification of 3-dimensional (3D) position for landfilled solid wastes. Using the landfill operation and performance management system based on 3D laser scanner delivers information about landfill volume, settlement, landfill density, and current landfill capacity to landfill operators in real time, resulting in optimum space utilization. Ultimately, this system would dramatically reduce exposure of landfill operators to hazardous materials and improve the productivity of landfill operations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.909-919
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• 2014

License plate (LP) detection is the most imperative part of an automatic LP recognition (LPR) system. Typical LPR contains two steps, namely LP detection (LPD) and character recognition. In this paper, we propose an efficient Vehicle-to-LP detection framework which combines with an adaptive GMM (Gaussian Mixture Model) and a cascade of boosted classifiers to make a faster vehicle LP detector. To develop a background model by using a GMM is possible in the circumstance of a fixed camera and extracts the motions using background subtraction. Firstly, an adaptive GMM is used to find the region of interest (ROI) on which motion detectors are running to detect the vehicle area as blobs ROIs. Secondly, a cascade of boosted classifiers is executed on the blobs ROIs to detect a LP. The experimental results on our test video with the resolution of $720{\times}576$ show that the LPD rate of the proposed system is 99.14% and the average computational time is approximately 42ms.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.1-8
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• 2011

Automatic vehicle license plate recognition technology is widely used in gate control and parking control of vehicles, and police enforcement of illegal vehicles. However inherent geometric information of the license plate can be transformed in the vehicle images due to the slant and the sunlight or lighting environment. In this paper, a distortion invariant vehicle license plate extraction and recognition algorithm is proposed. First, a binary image reserving clean character strokes can be achieved by using a DoG filter. A plate area can be extracted by using the location of consecutive digit numbers that reserves distortion invariant characteristic. License plate is recognized by using neural networks after geometric distortion correction and image enhancement. The simulation results of the proposed algorithm show that the accuracy is 98.4% and the average speed is 0.05 seconds in the recognition of 6,200 vehicle images that are obtained by using commercial LPR system.