• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.279-280
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• 2024

본 논문에서는 주행 중 차량의 상태를 실시간으로 모니터링함과 동시에, 페달 조작 여부를 확인할 수 있는 영상 촬영 및 저장 시스템을 제안한다. 개발된 차량 운행 정보 블랙박스는 블루투스 OBD2 커넥터를 통해 차량의 PID 값을 식별하고 수집한다. 이 데이터는 비동기 방식으로 처리되며, 라즈베리파이와 7인치 터치 디스플레이를 이용해 운전자에게 한눈에 보일 수 있는 형태로 정보를 제공한다. 특히, 멀티스레드를 활용하여 ECU 정보를 페달 조작 여부 영상에 표시하는 동시에 녹화하고, CSV 파일로 SD 카드에 실시간으로 저장한다. 수집된 차량 데이터와 영상 데이터는 예기치 못한 사고 발생 시 운전자의 과실 비율 측정과 대처행동을 입증하는 데 중요한 역할을 할 것으로 기대되며, 차량 정비 시 참고 자료로 활용될 수 있다.

• Journal of the Institute of Convergence Signal Processing
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• v.17 no.1
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• pp.10-17
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• 2016

GPS location information storage included in the HD black box is using a unique manner for each manufacturer does not have a specific standard. In this paper, in order to overcome the limitations of the storage space and thereby the image quality according to store GPS position information deteriorate to solve the problems that cause, we propose the location information concealment method included in the HDTV video content using a essential hidden region. HDTV video content is a Border Extender of 8 lines in the frame to the bottom of the compression will be required. This was inserted into the image of a gray scale used in block form in order to space the current position information is concealed to prevent image degradation. The proposed method was confirmed using real HD black box, there are more difficult to interpret the format of the ASCII code re-edit the location information when the compression effect disappears with the existing security zones added. Therefore, the proposed method is suitable for location-based services, such as Facebook or Youtube videos.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.9-15
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• 2013

In this paper, automatic recognition of an accident and whether service delivery and risk driving through the giving of the driver to correct driving habits before and after the accident to reproduce highly scalable video Smart Mobile Blackbox DVR (SMBD, Smart Mobile Blackbox DVR) Computer of the model was designed. SMBD on embedded systems equipped with wireless capabilities to sleep in the car accident point and the image information by wireless communications, by notification in the control center, 24-hour emergency rescue service and traffic information can be provided. The vehicle ECU (Electronic Control Unit) of the vehicle information and sensor data in conjunction with wireless eCall (Emergency Call) services can be realized.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.229-235
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• 2017

In recent years, research has been actively carried out to recognize and recognize objects based on a large amount of data. In this paper, we propose a system that extracts objects that are thought to be obstacles in road driving images and recognizes them by car, man, and motorcycle. The objects were extracted using Optical Flow in consideration of the direction and size of the moving objects. The extracted objects were recognized using Alexnet, one of CNN (Convolutional Neural Network) recognition models. For the experiment, various images on the road were collected and experimented with black box. The result of the experiment showed that the object extraction accuracy was 92% and the object recognition accuracy was 96%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.49-54
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• 2024

Data imbalance is a common issue encountered in classification problems, stemming from a significant disparity in the number of samples between classes within the dataset. Such data imbalance typically leads to problems in classification models, including overfitting, underfitting, and misinterpretation of performance metrics. Methods to address this issue include resampling, augmentation, regularization techniques, and adjustment of loss functions. In this paper, we focus on loss function adjustment, particularly comparing the performance of various configurations of loss functions (Cross Entropy, Balanced Cross Entropy, two settings of Focal Loss: 𝛼 = 1 and 𝛼 = Balanced, Asymmetric Loss) on Multi-Class black-box video data with imbalance issues. The comparison is conducted using the I3D, and R3D_18 models.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.132-145
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• 2022

This study was based on the black box images of traffic accidents on highways, cluster analysis and prediction model comparisons were carried out. As analysis data, vehicle driving behavior and road surface conditions that can grasp road and traffic conditions just before the accident were used as explanatory variables. Considering that traffic accident data is affected by many factors, cluster analysis reflecting data heterogeneity is used. Each cluster classified by cluster analysis was divided based on the ratio of the severity level of the accident, and then an accident prediction evaluation was performed. As a result of applying the Logit model, the accident prediction model showed excellent predictive ability when classifying groups by cluster analysis and predicting them rather than analyzing the entire data. It is judged that it is more effective to predict accidents by reflecting the characteristics of accidents by group and the severity of accidents. In addition, it was found that a collision accident during stopping such as a secondary accident and a side collision accident during lane change act as important driving behavior variables.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1762-1769
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• 2021

The final goal of this study is to develop a system that can analyze whether a wanted vehicle is a criminal vehicle from images collected from black boxes, smartphones, CCTVs, and so on. Data collection was collected using a self-developed black box. The used data in this study has used a total of 83,753 cases such as the eight vehicle types(truck, RV, passenger car, van, SUV, bus, sports car, electric vehicle) and 434 vehicle models. As a result of vehicle recognition using YOLO v5, mAP was found to be 80%. As a result of identifying the vehicle model with ReXNet using the self-developed black box, the accuracy was found to be 99%. The result was verified by surveying field police officers. These results suggest that improving the accuracy of data labeling helps to improve vehicle recognition performance.

• Electronics and Telecommunications Trends
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• v.29 no.4
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• pp.21-29
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• 2014

최근에 스마트폰, CCTV, 블랙박스, 고화질 카메라 등으로부터 수집되는 영상 데이터의 양이 급격히 증가하고 있어 이에 따른 비정형 영상 빅데이터를 기반으로 인물이나 사물 등을 인식하여 의미있는 정보를 추출하고 내용을 시각적으로 분석하고 활용하기 위한 요구사항이 증대되고 있다. 영상 빅데이터 분석기술은 이러한 대규모 영상들에 대해 학습 및 분석을 수행하여 원하는 영상을 검색하거나 이벤트 발생 등의 상황인식을 위한 제반 기술들을 말한다. 본고에서는 영상인식을 위한 학습기술 및 영상 빅데이터 분석기술의 현황 및 관련 이슈들에 관하여 살펴보고자 한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.1-11
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• 2021

This paper presents a procedure to extract lane line models using a set of proposed methods. Firstly, an image warping method based on homography is proposed to transform a target image into an image which is efficient to find lane pixels within a certain region in the image. Secondly, a method to use the combination of the results of edge detection and HSL (Hue, Saturation, and Lightness) transform is proposed to detect lane candidate pixels with reliability. Thirdly, erroneous candidate lane pixels are eliminated using a selection area method. Fourthly, a method to fit lane pixels to quadratic polynomials is proposed. In order to test the validity of the proposed procedure, a set of black-box images captured under varying illumination and noise conditions were used. The experimental results show that the proposed procedure could overcome the problems of color-only and edge-only based methods and extract lane pixels and model the lane line geometry effectively within less than 0.6 seconds per frame under a low-cost computing environment.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.658-660
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• 2016

최근 디지털 기술의 발달로 인하여 실외 환경에서 획득된 영상은 민수분야 및 군사분야 등과 같이 다양한 목적에 따라 활용되는 분야의 폭이 넓어지고 있다. 교통정보 수집장치, 차량 블랙박스, 산불 및 지진관측, 선락/해안경비 감시, 국경 및 군사표적이동 감시 등의 목적에 의해 촬영된 영상들은 대부분 획득된 영상을 통해서 분석 및 판독의 과정을 거쳐서 각 원하는 정보 획득에 목적을 두고 있다. 하지만 실외에서 촬영된 영상은 실내에서 촬영된 영상에 비해서 기상에 따른 환경적인 요인에 노출이 쉽게 됨으로써 영상에 대한 화질 저하가 발생하는 문제점이 존재한다. 본 논문에서는 화질저하의 원인이 되는 다양한 요인 중에서도 대기중에 존재하는 먼지, 물방울 연무, 안개, 연기 등으로 인해 빛이 산란됨으로써 밝기 값을 왜곡시키는 문제점에 대한 해결 방법을 제안한다.