• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.6
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• pp.1495-1502
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• 2015

Dashboard camera is important system to store the variable data that not only video but also non-visual information that state of vehicle such as accelerometer, speed, direction. Non-visual information include variable data that can't visualization, so it used important evidence to figure out the situation in accident. It could be missed to non-visual information what can be prove the case in the just digital video forensic procedure. In this paper, We proposal the digital forensic analysis procedure for dashboard camera to all data in dashboard camera extract and analysis data for investigating traffic accident case. And I analyze to some products in with this digital forensic analysis procedure.

• Journal of Information Technology Services
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• v.15 no.4
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• pp.97-109
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• 2016

The rapid deployment of millions of mobile sensors and smart devices has resulted in high demand for opportunistic encounter-based networking. For the cooperative video surveillance of dashboard cameras in nearby vehicles, a fast and energy-efficient asynchronous neighbor discovery protocol is indispensable because a dashboard camera is an energy-hungry device after the vehicle's engine has turned off. In the existing asynchronous neighbor discovery protocols, all nodes always try to discover all neighbors. However, a dashboard camera needs to discover nearby dashboard cameras when an event is detected. In this paper, we propose a fast and energy-efficient asynchronous neighbor discovery protocol, which enables nodes : 1) to have different roles in neighbor discovery, 2) to discover neighbors within a search range, and 3) to report promptly the exact discovery result. The proposed protocol has two modes: periodic wake-up mode and active discovery mode. A node begins with the periodic wake-up mode to be discovered by other nodes, switches to the active discovery mode on receiving a neighbor discovery request, and returns to the periodic wake-up mode when the active discovery mode finishes. In the periodic wake-up mode, a node wakes up at multiples of number ${\alpha}$, where ${\alpha}$ is determined by the node's remaining battery power. In the active discovery mode, a node wakes up for consecutive ${\gamma}$ slots. Then, the node operating in the active discovery mode can discover all neighbors waking up at multiples of ${\beta}$ for ${\beta}{\leq}{\gamma}$ within ${\gamma}$ time slots. Since the proposed protocol assigns one half of the duty cycle to each mode, it consumes equal to or less energy than the existing protocols. A performance comparison shows that the proposed protocol outperforms the existing protocols in terms of discovery latency and energy consumption, where the frequency of neighbor discovery requests by car accidents is not constantly high.

• Journal of Information Technology Services
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• v.21 no.2
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• pp.27-41
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• 2022

In a fiercely competitive environment, quality is a key factor that enables dashboard camera makers to maintain their competitive advantage. Quality affects consumer satisfaction, brand loyalty, and firm performance. Therefore, to remain competitive, it is important that manufacturers maintain product quality that meets consumer expectations. To this end, it is necessary to investigate customer preferences and product performance in terms of product quality and to properly allocate resources to improve the quality level such that the firm can maintain a competitive advantage. In this paper, we proposed the various ways in which manufacturing firms can determine which quality dimensions need improvement in order to secure competitiveness. To this end, we analyzed a case study of Urive to develop a quality improvement strategy through importance performance competitor analysis (IPCA). Urive's IPCA results showed that 14 quality dimensions, namely performance, size, price, ease of use, country of origin, manufacturer, brand, product certificate, warranty, distribution channel, market share, reliability, durability, and conformance, were not absolutely competitive compared with those of Mando, Inavi, and Finevu. In terms of color, Urive had an absolute competitive advantage over Mando, but not Inavi and Finevu. Urive's appearance was more competitive than Mando's, but not Inavi's and Finevu's. In terms of advertisement and serviceability, Urive was absolutely less competitive than Mando and Inavi, but had a competitive advantage over Finevu. Therefore, it is necessary to put resources and time as the first priority for performance, reliability, and durability, which have a large performance difference in common among the three brands. The quality dimensions in which resources and time need to be put in second place are price and ease of use, which have a large performance difference in common among the two brands.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.7-7
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• 2000

This paper describes a mirror control system that can adjust the location of side and room mirror of the vehicle automatically using 3D coordinates to monitor the location of driver's eyes. Through analysis of the image inputted by two B/W CCD camera and infrared lamps installed on top of the driver's dashboard, we can estimate the values of 3D coordinate of the driver's eyes. Using these values, this system can determine the absolute position of each mirror and activate each actuator to the appropriate position. The stereo vision system can detect the driver's eyes whether it is day or night by virtue of infrared Lamps. We have tested this system using 10 drivers who drive a car currently, and most of the drivers were satisfied with the convenience of this system.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1455-1458
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• 2015

위 논문은 전방 카메라에 촬영된 내차의 전방 보닛 영역을 인식하는 알고리즘을 제안한다. 현재 스마트카나 무인카 등에서 사물인식을 위한 영상처리의 필요가 증가하고 있는데 영상처리의 실시간성을 위해서 ROI를 잘 한정할 필요가 있다. 이때 본 연구에서 제안한 보닛 영상을 효과적으로 검색 대상에서 배제하면 속도와 정확도에 도움이 될 것이다. 제안된 보닛 검출 방법의 핵심은, 사다리꼴을 형성하는 3개의 직선을 검출하여 보닛 영역 후보들을 찾고 이를 여러 프레임에 걸친 일관성을 관찰하여 종합 판단하는 것이다. 다양한 블랙박스 카메라에 촬영된 영상에 대해서 실험한 결과 실제 보닛과의 인식결과의 차이는 평균 5 픽셀이다.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.485-492
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• 2020

Information on the energy consumption of buildings that can be obtained through conventional methods is limited. Therefore, this study aims to develop a model that can support decision making about building facility management through digital transformation technologies. Through the IoT sensor, the building's energy data and indoor air quality data are collected, and the monitored data is visualized through the ELK Stack and produced as a dashboard. In addition, the target building is photographed with a 360-degree camera and maps using a tool to create a 360-degree tour. Using such digital transformation technologies, users of buildings can obtain various information in real time without visiting buildings directly. This can lead to changes in actions or actions for building management, supporting facility management decisions, and consequently reducing building energy consumption.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.6-9
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• 2015

현대인들의 생활수준 향상과 기술의 발전에 따라 보안, 안전 등 미처 고려되지 않던 분야들이 다양한 영역에서 부각되면서 CCTV, IP카메라, 차량용 블랙박스와 같은 영상기반 시스템에 대한 시장수요가 증가하고 있다. 이에 맞춰 다양한 영상기반 시스템들이 개발되고, 개발 단계에서 낭비되는 시간을 줄이기 위해 프로토타이핑의 필요성이 대두되고 있지만 기존의 프로토타이핑을 위한 도구는 비용이나 속도측면에서 제한적이다. 본 논문에서는 영상기반 시스템 중 블랙박스의 하드웨어를 풀 시스템 에뮬레이터를 이용하여 모델링하고 수행되는 명령어 추출을 통해 시스템의 특성을 예측할 수 있는 하드웨어 프로토타이핑 도구를 제안한다. 또한 ARM 시스템용으로 컴파일 된 프로그램의 실행 여부를 확인하고, 프로그램을 구성하는 명령어와 추출도구를 통해 추출된 명령어를 비교하여 동작을 확인한다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.114-123
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• 2023

With the development of computer vision technology, video sensors such as CCTV are detecting incident. However, most of the current incident have been detected based on existing fixed imaging equipment. Accordingly, there has been a limit to the detection of incident in shaded areas where the image range of fixed equipment is not reached. With the recent development of edge-computing technology, real-time analysis of mobile image information has become possible. The purpose of this study is to evaluate the possibility of detecting expressway emergencies by introducing computer vision technology to dash cam. To this end, annotation data was constructed based on 4,388 dash cam still frame data collected by the Korea Expressway Corporation and analyzed using the YOLO algorithm. As a result of the analysis, the prediction accuracy of all objects was over 70%, and the precision of traffic accidents was about 85%. In addition, in the case of mAP(mean Average Precision), it was 0.769, and when looking at AP(Average Precision) for each object, traffic accidents were the highest at 0.904, and debris were the lowest at 0.629.