• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.713-725
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• 2019

Automatic License Plate Recognition (ALPR) is a technology required for many applications such as Intelligent Transportation Systems and Video Surveillance Systems. Most of the studies have studied were about the detection and recognition of license plates on cars, and there is very little about detecting and recognizing license plates on motorbikes. In the case of a car, the license plate is located at the front or rear center of the vehicle and is a straight or slightly sloped license plate. Also, the background of the license plate is mainly monochromatic, and license plate detection and recognition process is less complicated. However since the motorbike is parked by using a kickstand, it is inclined at various angles when parked, so the process of recognizing characters on the motorbike license plate is more complicated. In this paper, we have developed a 2-stage YOLOv2 algorithm to detect the area of a license plate after detection of a motorbike area in order to improve the recognition accuracy of license plate for motorbike data set parked at various angles. In order to increase the detection rate, the size and number of the anchor boxes were adjusted according to the characteristics of the motorbike and license plate. Image warping algorithms were applied after detecting tilted license plates. As a result of simulating the license plate character recognition process, the proposed method had the recognition rate of license plate of 80.23% compared to the recognition rate of the conventional method(YOLOv2 without image warping) of 47.74%. Therefore, the proposed method can increase the recognition of tilted motorbike license plate character by using the adjustment of anchor boxes and the image warping which fit the motorbike license plate.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.338-340
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• 2000

자동차 번호판 인식 시스템은 영상획득, 번호판 영역 추출, 추출된 번호판 영역의 전처리, 문자부분 영역화, 문자인식 등의 5가지 핵심부분으로 구성된다. 그 중에서도 번호판 영역 추출, 추출된 영역의 전처리, 문자부분 영역화의 정확성은 전체 시스템 인식률에 지대한 영향을 줄 수 있는 부분으로써 그 정확성이 요구된다. 이에 본 논문에서는 컴퓨터 비젼 분야 중의 하나인 영상처리 기법을 사용하여 명암의 변화에도 문자를 잘 추출할 수 있는 Dynamic Adaptive Threshold 방법을 사용하여 추출된 번호판 영역을 이진화하고, 정확하게 문자 부분을 영역화하기 위한 방법으로 누적분포와 번호판 문자배열 특성을 이용한 방법을 제안한다. 그리고 추출되어진 문자는 ART2 신경망을 이용하여 인식한다.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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• pp.534-539
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• 2013

Recently, the number of vehicles equipped with cameras that are generally used to recognize surroundings is increasing. For robust recognition, a huge amount of tests under various environments are performed. Furthermore, the installation position or orientation of the camera is also changed depending on the vehicle. This change also accompanies many tests. Correspondingly, a large cost and a great deal of manpower are required to perform these tests. This paper proposes a method to cut these costs while conducting enough tests through the construction of a database of videos and a geometric transformation of images.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.568-570
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• 2002

최근, 분류기 쪽에서는 모듈라 학습을 이용한 방법들에 대해서 상당한 관심이 모아지고 있다. 모듈라 학습 방법은 divide and conquer 개념에 바탕을 두고 있기 때문에 복잡한 문제에 대해서 학습 질 측면이나 학습 속도 면에서 단일 분류기에 비해 좋은 결과들을 나타내고 있다. 인공신경망을 이용한 분류 방법 쪽에서도 이러한 연구들이 이루어지고 있다. 본 논문에서는 번호판 인식을 위한 간단한 형태의 모듈라 신경망을 제안하고 이의 성능을 평가하였다. 실험 결과, 일반적인 차량 번호판의 영상에서 성공적인 결과를 보였으며, 잡음에 의한 훼손된 번호판도 좋은 인식 결과를 보였다. 또한 인식률 측면 뿐만 아니라 학습 속도 면에서도 상당한 이득이 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.383-385
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• 2000

차량 번호판 문자영상으로부터 여러 가지 특징 추출 방법을 조합하여 입력특징소를 재구성하고, 신경망을 이용하여 문자를 인식한다. 속도 개선을 위해 특별한 전처리 과정없이 이치화와 크기 정규화만을 수행한 후 그물망 방법과 BLT 방법, 정규화된 투영값 특정 방법을 조합하여 입력특징소를 구성한다. 본 연구에서는 숫자 인식에서 그물망 방법과 BLT 방법을 이용하여 잡음으로 인한 유사 문자의 오인식을 해결하였고, 문자 인식에서는 정규화된 투영값 특징을 이용하여 문자의 유형을 분류한 후 자소를 개별적으로 인식하였다. 이로써 모음 인식 경우에 중요한 역할을 하는 작은 획의 영역에 BLT 방법을 사용함으로 기존 연구에서의 모음 오인식 문제를 해결하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.3
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• pp.68-83
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• 2019

The number of pedestrian traffic accident fatalities is three times the number of car accidents in South Korea. Serious accidents are caused especially at intersections when the vehicle turns to their right. Various pedestrian collision warning services have been developed, but they are insufficient to prevent dangerous pedestrians. In this study, P2CWS is developed to warn approaching vehicles based on the pedestrians' characteristics. In order to evaluate the performance of the service, actual pedestrian data were collected at the intersection of Daejeon, and comparative analysis was carried out according to pedestrian characteristics. As a result, the performance analysis showed a higher accordance when the characteristics of the pedestrian is considered. Accordingly, we can conclude that identifying pedestrian characteristics in predicting the pedestrian crossing is important.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.142-149
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• 2016

This paper proposes a method to detect LP(License Plate) of vehicles in indoor and outdoor parking lots. In restricted environment, there are many conventional methods for detecting LP. But, it is difficult to detect LP in natural and complex scenes with background clutters because several patterns similar with text or LP always exist in complicated backgrounds. To verify the performance of LP text detection in natural images, we apply MB-LGP feature by combining with ensemble machine learning algorithm in purpose of selecting optimal features of small number in huge pool. The feature selection is performed by adaptive boosting algorithm that shows great performance in minimum false positive detection ratio and in computing time when combined with cascade approach. MSER is used to provide initial text regions of vehicle LP. Throughout the experiment using real images, the proposed method functions robustly extracting LP in natural scene as well as the controlled environment.

• The Journal of Korea Robotics Society
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• v.8 no.1
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• pp.51-57
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• 2013

Recently, the number of jellyfish has been rapidly grown because of the global warming, the increase of marine structures, pollution, and etc. The increased jellyfish is a threat to the marine ecosystem and induces a huge damage to fishery industries, seaside power plants, and beach industries. To overcome this problem, a manual jellyfish dissecting device and pump system for jellyfish removal have been developed by researchers. However, the systems need too many human operators and their benefit to cost is not so good. Thus, in this paper, the design, implementation, and experiments of autonomous jellyfish removal robot system, named JEROS, have been presented. The JEROS consists of an unmanned surface vehicle (USV), a device for jellyfish removal, an electrical control system, an autonomous navigation system, and a vision-based jellyfish detection system. The USV was designed as a twin hull-type ship, and a jellyfish removal device consists of a net for gathering jellyfish and a blades-equipped propeller for dissecting jellyfish. The autonomous navigation system starts by generating an efficient path for jellyfish removal when the location of jellyfish is received from a remote server or recognized by a vision system. The location of JEROS is estimated by IMU (Inertial Measurement Unit) and GPS, and jellyfish is eliminated while tracking the path. The performance of the vision-based jellyfish recognition, navigation, and jellyfish removal was demonstrated through field tests in the Masan and Jindong harbors in the southern coast of Korea.

• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.411-418
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• 2003

The proposed system in this paper uses a camera attached to a mobile device in order to inquire a car and track its location anywhere. To do this, the system recognizes and verifies license plates on the front and back of a cu. The plates are scanned by the camera attached to a mobile device. The technology enables us to detect a car registration number and to transmit the number along with the location of the device to a server through a wireless communication network. The information of a car obtained through the terminal is encoded and transmitted to a server in a remote place through a wireless communication network also. The car registration number and its location information are decoded and transmitted as a text to the server in a remote place. In order to track a user´s location through spatial relative distance estimated in real-time, the server uses the spatial and attribute information which are the most prior to the desired data value. With this property information, the right location can be calculated.

• Journal of IKEEE
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• v.23 no.4
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• pp.1423-1429
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• 2019

Recently, a system that supports efficient interaction, a human machine interface (HMI), has become a hot topic. In this paper, we propose a new real time low-cost hand gesture control system as one of vehicle interaction methods. In order to reduce computation time, depth information was acquired using a time-of-flight (TOF) camera because it requires a large amount of computation when detecting hand regions using an RGB camera. In addition, fourier descriptor were used to reduce the learning model. Since the Fourier descriptor uses only a small number of points in the whole image, it is possible to miniaturize the learning model. In order to evaluate the performance of the proposed technique, we compared the speeds of desktop and raspberry pi2. Experimental results show that performance difference between small embedded and desktop is not significant. In the gesture recognition experiment, the recognition rate of 95.16% is confirmed.