• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.771-783
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• 2012

This paper proposes a scheme to collect video data of the vehicle black box in order to strengthen the public safety. The existing schemes, such as surveillance system with the fixed CCTV and car black box, have privacy issues, network traffic overhead and the storage space problems because all video data are sent to the central server. In this paper, the central server only collects the video data related to the accident or the criminal offense using the GPS information and time in order to investigation of the accident or the criminal offense. The proposed scheme addresses the privacy issues and reduces network traffic overhead and the storage space of the central server since the central server collects the video data only related to the accident and the criminal offense. The implementation and experiment shows that our service is feasible. The proposed service can be used as a component of remote surveillance system to prevent the criminal offense and to investigate the criminal offense.

• 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.

• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.93-99
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• 2020

A majority of car crash is affected by careless driving that causes extensive economic and social costs, as well as injuries and fatalities. Thus, the research of precise crash detection systems is very significant issues in automotive safety. A lot of crash detection algorithms have been developed, but the coverage of these algorithms has been limited to few scenarios. Road scenes and situations need to be considered in order to expand the scope of a collision detection system to include a variety of collision modes. The proposed algorithm effectively handles the x, y, and z axes of the sensor, while considering time and suggests a method suitable for various real worlds. To reduce nuisance and false crash detection events, the algorithm discriminated between driving mode and parking mode. The performance of the suggested algorithm was evaluated under various scenarios, and it successfully discriminated between driving and parking modes, and it adjusted crash detection events depending on the real scenario. The proposed algorithm is expected to efficiently manage the space and lifespan of the storage device by allowing the vehicle's black box system to store only necessary crash event's videos.

• 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%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.554-557
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• 2010

Pay Per Drive System can be a new solution for improving the traffic congestion and air pollution by not only taxing on the driving distance but also taxing different proportion with driver' s location and time. Thus it gives us new concept of the area and time of traffic jam, as a result, we can find a natural and efficient transportation mechanism and reduced air pollution. Also this system can trace the driver' s location by GPS, it can provide Black Box function on every car on the road. We propose the method of advanced transportation service by Pay Per Drive System.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.334-338
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• 2012

현재 우리나라에서는 일반 자동차 부품처럼 보급되고 있는 자동차 블랙박스는 하드웨어 기반의 장비로, 추후 애로사항 수정 및 추가기능 업데이트가 불가능하다. 또한 여러 가지 물리적 문제로 인하여 많은 운전자들이 불편을 겪고 있다. 현재 출시된 블랙박스는 GPS와 영상정보만 수집하여 추후 저장된 사고기록을 바탕으로 사고원인의 규명 할 때 신뢰성이 낮은 결과가 나올 수 있다. 본 논문에서는 앞에서 서술한 기존 자동차 블랙박스의 문제점을 해결하고자 기존의 블랙박스에서 취급하는 정보 이외에 OBD프로토콜을 통해 수집되는 자동차 내부정보를 다루는 안드로이드 OS용 자동차 블랙박스 어플리케이션을 개발하고자 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2677-2685
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• 2013

Car running data collected from the vehicle is a native image data and sensing data of it. Hence, it can be used as a set of objective data based on which events that took place outside the car can be analyzed and determined. In this paper, we designed and implemented a emergency situation detection system to sense, store, and analyze signals related to car movements, driver's various operation states, collision pulse, etc when a car collision accident occurs on the actual road by sensing and analyzing the car movements and driver's operation status. The suggested system provides information on the driver's reaction right before the collision, operation state of the vehicle, and physical movement. The collected and analyzed data on vehicle running can be utilized to clarify the cause of a collision accident and to handle it in a just manner. Besides, it can contribute to grasping and correcting wrong driving habits of the driver and to saving.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.621-626
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• 2015

This paper presents a camera calibration method in order to estimate the lane detection and inter-vehicle distance estimation system for an automotive safety driving system. In order to implement the lane detection and vision-based inter-vehicle distance estimation to the embedded navigations or black box systems, it is necessary to consider the computation time and algorithm complexity. The process of camera calibration estimates the horizon, the position of the car's hood and the lane width for extraction of region of interest (ROI) from input image sequences. The precision of the calibration method is very important to the lane detection and inter-vehicle distance estimation. The proposed calibration method consists of three main steps: 1) horizon area determination; 2) estimation of the car's hood area; and 3) estimation of initial lane width. Various experimental results show the effectiveness of the proposed method.

• Journal of Broadcast Engineering
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• v.27 no.7
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• pp.999-1010
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• 2022

The increase in vehicle purchases worldwide is having a very significant impact on the availability of parking spaces. In particular, since it is difficult to secure a parking space in an urban area, it may be of great help to the driver to check vehicle parking information in advance. However, the current parking lot information is still operated semi-manually, such as notifications. Therefore, in this study, we propose a system for detecting a parking space using a relatively simple image processing method based on an image taken from the sky and evaluate its performance. The proposed method first converts the captured RGB image into a black-and-white binary image. This is to simplify the calculation for detection using discrete information. Next, a morphological operation is applied to increase the clarity of the binary image, and a template mask in the form of a bounding box indicating a parking space is applied to check the parking state. Twelve image samples and 2181 total of test, were used for the experiment, and a threshold of 40% was used to detect each parking space. The experimental results showed that information on the availability of parking spaces for parking users was provided with an accuracy of 95%. Although the number of experimental images is somewhat insufficient to address the generality of accuracy, it is possible to confirm the possibility of parking space detection with a simple image processing method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.1
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• pp.142-152
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• 2016

GPS is not able to be used for indoor positioning and currently most of techniques emerging to overcome the limit of GPS utilize private wireless networks. However, these methods require high costs for installation and maintenance, and they are inappropriate to be used in the place where precise positioning is needed as in indoor parking lots. This paper proposes a vehicular indoor positioning method based on QR-code recognition. The method gets an absolute coordinate through QR-code scanning, and obtain the location (an relative coordinate) of a black-box camera using the tilt and roll angle correction through affine transformation, scale transformation, and trigonometric function. Using these information of an absolute coordinate and an relative one, the precise position of a car is estimated. As a result, average error of 13.79cm is achieved and it corresponds to just 27.6% error rate in contrast to 50cm error of the recent technique based on wireless networks.