• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.354-357
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• 2020

최근 스포츠 경기나 차량 블랙박스 등에서 비디오를 이용한 판정이 점차 확대되고 있지만, 일반 카메라로 촬영된 비디오에서 정확한 판정을 하기 어려울 때가 빈번히 발생한다. 초고속 카메라로 촬영한 슬로우 모션 비디오를 이용할 수 있다면 판정의 정확성을 향상시킬 수 있을 것이다. 본 논문에서는 일반 카메라로 촬영한 비디오로부터 마치 초고속 카메라로 촬영한 것과 같은 슬로우 모션 비디오를 생성하여 제공하는 서비스를 제안한다. 제안 방법은 NVIDIA에서 개발한 Super Slomo 기술을 기반으로, 초당 30장의 표준 비디오를 초당 60장에서 240장까지의 고품질 슬로우 모션 비디오로 변환한다. 이 기술은 시간적으로 이웃한 두 영상을 입력하여 딥 러닝 기반으로 중간 프레임을 보간함으로써 프레임율을 향상시킨다. 또한 본 논문에서는 Super Slomo 기술에 FP16을 적용하여 처리속도를 향상 시켰으며, 웹 서버를 이용하여 비디오를 업로드하고 슬로우 모션으로 변환된 비디오를 다운로드 할 수 있는 사이트를 구현했다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.320-323
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• 2014

Today is going with the advancement of intelligent transportation systems and traffic environment and helping to provide safe and convenient service through a mobile device work with the popularization of the vehicle black box. The traffic flow by a variety of causes is constantly changing, it is often unable to prepare the driver, depending on external factors can not be controlled by the power of the public, leading to a major accident. The system needs to pass the real-time weather data in the inter-operator to prevent this. The proposed detection algorithm weather information delivered real-time weather information for this paper. The weather condition is detected by using the contrast between the histogram of the motion of the wiper and the clear day algorithm. In general, the wiper is worked in extreme weather conditions that will have a value different contrast due to rain or snow. Situation was considered clear, snowy conditions, such as using it on a rainy situation. First, designated as ROI (Region Of Interest) of the minimum area that can be detected in order to reduce the amount of calculation for the wiper, the wiper, which was detected through the operation of the threshold Thresholding the brightness of the vehicle wiper. In addition, we distinguish the value of each meteorological situation by using contrast. Results was obtained to 80% for the snow conditions, a rainy situation.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.116-125
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• 2014

Road conditions can provide important information for driving safety in driving assistance systems. The input images usually include unnecessary information and they need to be analyzed only in a region of interest (ROI) to reduce the amount of computation. In this paper, a vision-based road ROI determination algorithm is proposed to detect the road region using the positional information of a vanishing point and line segments. The line segments are detected using Canny's edge detection and Hough transform. The vanishing point is traced by a Kalman filter to reduce the false detection due to noises. The road ROI can be determined automatically and adaptively in every frame after initialization. The proposed method is implemented using C++ and the OpenCV library, and the road ROIs are obtained from various video images of black boxes. The results show that the proposed algorithm is robust.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.6
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• pp.9-16
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• 2017

An Algorithm for Lane Detection and Lane Departure Warning for a Vehicle Driving on Roads is proposed in This Paper. Using Images Obtained from On-board Cameras for Lane Detection has Some Difficulties, e.g. the Increase of Fault Detection Ratio Due to Symbols on Roads, Missing Yellow Lanes in the Tunnel due to a Similar Color Lighting, Missing Some Lanes in Rainy Days Due to Low Intensity of Illumination, and so on. The Proposed Algorithm has been developed Focusing on Solving These Problems. It also has an Additional Function to Determine How much the Vehicle is leaning to any Side between The Lanes and, If Necessary, to Give a Warning to a Driver. Experiments Using an Image Database Built by Collecting with Vehicle On-board Blackbox in Six Different Situations have been conducted for Validation of the Proposed Algorithm. The Experimental Results show a High Performance of the Proposed Algorithm with Overall 97% Detection Success Ratio.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.12
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• pp.123-129
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• 2014

In accordance with the development of various convergence devices, cameras are being used in many types of the systems such as security system, driver assistance device and so on, and a lot of people are exposed to these system. Therefore the system should be able to recognize the human behavior and support some useful functions with the information that is obtained from detected human behavior. In this paper we use a machine learning approach based on 2D image and propose the human behavior pattern recognition methods. The proposed methods can provide valuable information to support some useful function to user based on the recognized human behavior. First proposed one is "phone call behavior" recognition. If a camera of the black box, which is focused on driver in a car, recognize phone call pose, it can give a warning to driver for safe driving. The second one is "looking ahead" recognition for driving safety where we propose the decision rule and method to decide whether the driver is looking ahead or not. This paper also shows usefulness of proposed recognition methods with some experiment results in real time.

• Journal of IKEEE
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• v.22 no.1
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• pp.46-52
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• 2018

The problem of traffic light detection and recognition has recently become one of the most important topics in various researches on autonomous driving. Most algorithms are based on colors to detect and recognize traffic light signals. These methods have disadvantage in that the recognition rate is lowered due to the change of the color of the traffic light, the influence of the angle, distance, and surrounding illumination environment of the image. In this paper, we propose machine learning based detection and recognition algorithm using shape information to solve these problems. Unlike the existing algorithms, the proposed algorithm detects and recognizes the traffic signals based on the morphological characteristics of the traffic lights, which is advantageous in that it is robust against the influence from the surrounding environments. Experimental results show that the recognition rate of the signal is higher than those of other color-based algorithms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.6
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• pp.1245-1255
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• 2009

Recently, to analyze the cause of the railway accident objectively and quickly and prevent the accident, many countries are legislating for the installation of the black box what we call an event recorder, which records information about the operation of railway vehicle. Thus, the study of the event recorder has been in progress. Moreover, the analysis software that can analyze and express the stored data in the event recorder is required for the correct decision on the accident. Therefore, in this paper, we presented a design of analysis software which analyzes the data, plays the audio and video in the event recorder system. This software can quickly and accurately identify the cause of the accident and recognize the driving patterns and habits of the driver according to the operating section. In addition, by analyzing the audio and video data simultaneously in the previous accident, we expect that it is possible to prevent accidents in advance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.104-114
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• 2016

Potholes have caused diverse problems such as wheel damage and car accident. A pothole detection technology is the most important to provide efficient pothole maintenance. The previous pothole detections have been performed by manual reporting methods. Thus, the problems caused by potholes have not been solved previously. Recently, many pothole detection systems based on video cameras have been studied, which can be implemented at low costs. In this paper, we propose a new pothole detection algorithm based on saliency map information in order to improve our previously developed algorithm. Our previous algorithm shows wrong detection with complicated situations such as the potholes overlapping with shades and similar surface textures with normal road surfaces. To address the problems, the proposed algorithm extracts more accurate pothole regions using the saliency map information, which consists of candidate extraction and decision. The experimental results show that the proposed algorithm shows better performance than our previous algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.427-430
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• 2012

최근 차량용 블랙박스, 자동 운전 시스템, 어라운드 뷰 시스템 등과 같은 운전자의 편의와 안전을 위한 장치 및 시스템들이 개발되고 있다. 현재 운전자를 위한 보조 시스템으로 구글(google)의 자동 운전 시스템(Auto Car Driving System)과 현대 모비스(hyundai mobis)의 AVM 시스템(Around View Monitoring System) 등의 다양한 차량용 편의장치 시스템들이 등장했다. 위와 같은 다양한 ECU들을 관리하기 위한 서버 및 저장 장치 역할을 할 수 있는 고사양의 Car PC의 장착이 필수적이다. 기존의 AVM 시스템은 차량 주변을 실시간으로 제공하기 위해 임베디드 또는 별도의 차량용 네트워크를 통해 임베디드 시스템 또는 SoC(System On Chip)형태의 하드웨어 기반으로 개발되고 있다. 하지만 고사양의 Car PC 기반에서는 별도의 비용없이 소프트웨어로 구현이 가능하다. 본 논문에서는 차량의 전 후 좌 우에 장착된 4대의 카메라로부터 입력된 차량 주변 상황을 한눈에 보여주는 AVM 시스템(Around-View Monitoring System)을 위한 카메라 보정 및 정합 처리 모듈 및 AVM 시스템을 Windows를 O/S로 하는 PC 내부에서 기존의 AVM 시스템을 이용하여 화면에 전 후 좌 우 버튼을 각각 만들어 버튼을 터치했을 때, 각 버튼에 해당되는 영상이 AVM 시스템과 함께 출력되도록 하거나 디스플레이에 Full 버전으로 출력되도록 S-UI(Smart User Interface)를 설계 및 구현한다. 제안하는 AVM 시스템과 기존의 AVM 시스템의 성능과 기능을 비교 분석함으로써 제안하는 영상 처리 모듈을 이용하여 추가 비용이 발생하지 않는 AVM 시스템의 구현 가능성을 검증한다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.3
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• pp.95-105
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• 2019

In recent years, image processing techniques for detecting road surface damaged spot have been actively researched. Especially, it is mainly used to acquire images through a smart phone or a black box that can be mounted in a vehicle and recognize the road surface damaged region in the image using several algorithms. In addition, in conjunction with the GPS module, the exact damaged location can be obtained. The most important technology is image processing algorithm. Recently, algorithms based on artificial intelligence have been attracting attention as research topics. In this paper, we will also discuss artificial intelligence image processing algorithms. Among them, an object detection method based on an region-based convolution neural networks method is used. To improve the recognition performance of road surface damage objects, 600 road surface damaged images and 1500 general road driving images are added to the learning database. Also, supervised learning using background object recognition method is performed to reduce false alarm and missing rate in road surface damage detection. As a result, we introduce a new method that improves the recognition performance of the algorithm to 8.66% based on average value of mAP through the same test database.