• Journal of Korean Society of Transportation
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• v.30 no.2
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• pp.127-136
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• 2012

Multi-car rear-end collision accidents have three categories: sequential collision from the rear which is commonly referred to as chain reaction collision, sequential collision from the front, and mixed-order collision. This paper suggests several effective methods of reconstruction analysis for multi-car rear-end collision accidents. First, by incorporating the traditional empirical method which uses vehicle damage caused by brake dive and passenger injuries, with results of theoretical analysis made within mechanics of rigid body, it is made possible for the method to be put to immediate practical use. A methodology to precisely analyze multi-car rear-end collision accidents was suggested using a simulation program simultaneously with a video event data recorder which is starting to be widely used in domestic vehicles. To go beyond the simple intuitive analysis of the video event data recorder, the simulation analysis based on the results of video analysis was executed to acquire various information, so that the causes and responsibility could be clearly stated.

• International Journal of Advanced Culture Technology
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• v.8 no.2
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• pp.146-158
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• 2020

As one of the successful brand activism ad campaigns in recent years, the current study focuses on the Always #LikeAGirl campaign that took on the issue of girls and female empowerment. As a viral video marketing campaign with YouTube as their main vehicle for campaign dissemination, this study examined how Always brand activism campaigns spread on YouTube by conducting a network analysis of YouTube video networks generated by the #LikeAGirl campaign spanning across five campaign periods. Quantifiable data (i.e., views, comments, likes, dislikes, user-generated videos) and structural network patterns show that the Always #LikeAGirl campaign was successful by both standards. Although the follow-up campaign periods were not as successful as the initial campaign, the substantial amount of views, comments, likes, and user-generated content showed that the consecutive campaigns still had impact. As shown through the network patterns, the main campaign ads were central in the diffusion of the campaign during the earlier periods but that role was passed onto the user-generated contents in the later periods. Implications of the findings and future social network analysis studies in brand advertising and brand activism campaigns are further discussed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.152-164
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• 2015

A rapid development of semiconductors, sensors and mobile network technologies has enable that the embedded device includes high sensitivity sensors, wireless communication modules and a video processing module for vehicular environment, and many researchers have been actively studying the smart car technology combined on the high performance embedded devices. The vehicle is increased as the development of society, and the risk of accidents is increasing gradually. Thus, the advanced driver assistance system providing the vehicular status and the surrounding environment of the vehicle to the driver using various sensor data is actively studied. In this paper, we design and implement the smart vehicular camera device providing the V2X communication and gathering environment information. And we studied the method to create the metadata from a received video data and sensor data using video analysis algorithm. In addition, we invent S-ROI, D-ROI methods that set a region of interest in a video frame to improve calculation performance. We performed the performance evaluation for two ROI methods. As the result, we confirmed the video processing speed that S-ROI is 3.0 times and D-ROI is 4.8 times better than a full frame analysis.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.93-101
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• 2023

In this paper, we propose a method of creating a 3D bounding box for an object using a vanishing point to increase the accuracy of object recognition in an image when recognizing an traffic object using a video camera. Recently, when vehicles captured by a traffic video camera is to be detected using artificial intelligence, this 3D bounding box generation algorithm is applied. The vertical vanishing point (VP1) and horizontal vanishing point (VP2) are derived by analyzing the camera installation angle and the direction of the image captured by the camera, and based on this, the moving object in the video subject to analysis is specified. If this algorithm is applied, it is easy to detect object information such as the location, type, and size of the detected object, and when applied to a moving type such as a car, it is tracked to determine the location, coordinates, movement speed, and direction of each object by tracking it. Able to know. As a result of application to actual roads, tracking improved by 10%, in particular, the recognition rate and tracking of shaded areas (extremely small vehicle parts hidden by large cars) improved by 100%, and traffic data analysis accuracy was improved.

• Journal of Korean Society of Transportation
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• v.23 no.5 s.83
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• pp.105-112
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• 2005

This paper not only tests VIDS(Video Image Detection System) performance by vehicle speed group but also suggests optimal VIDS height considering road and cost condition. The VIDS spreads over freeway and national highway and plays an important role in ITS(Intelligent Transportation Systems). As a result, speed data accuracy drops form 50kph vehicle speed and volume and occupancy data accuracy drop from 30kph. Lowest speed data accuracy is only 88%, but volume and occupancy accuracy are 75% and 77% respectively. The reason VIDS data accuracy drop by vehicle speed is gap distance decrease between vehicles. Therefore, this paper suggests $17m{\sim}21m$ for optimal VIDS height considering road and cost condition.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.92-98
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• 2022

This paper proposes a new method of the traffic signal detection and the recognition in an HSI color model. The proposed method firstly converts a ROI image in the RGB model to in the HSI model to segment the color of a traffic signal. Secondly, the segmented colors are dilated by the morphological processing to connect the traffic signal light and the signal light case and finally, it extracts the traffic signal light and the case by the aspect ratio using the connected component analysis. The extracted components show the detection and the recognition of the traffic signal lights. The proposed method is implemented using C language in Raspberry Pi 4 system with a camera module for a real-time image processing. The system was fixedly installed in a moving vehicle, and it recorded a video like a vehicle black box. Each frame of the recorded video was extracted, and then the proposed method was tested. The results show that the proposed method is successful for the detection and the recognition of traffic signals.

• Journal of the Semiconductor & Display Technology
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• v.14 no.2
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• pp.29-34
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• 2015

Traffic lights are common in cities and are important cues for the path planning of intelligent vehicles. In this paper, we propose a robust and efficient algorithm for recognizing traffic lights from video sequences captured by a low cost off-the-shelf camera. Instead of using color information for recognizing traffic lights, a shape based approach is adopted. In learning and detection phase, Histogram of Oriented Gradients (HOG) feature is used and a cascade classifier based on Adaboost algorithm is adopted as the main classifier for locating traffic lights. To decide the color of the traffic light, a technique based on histogram analysis in HSV color space is utilized. Experimental results on several video sequences from typical urban environment prove the effectiveness of the proposed algorithm.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.243-250
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• 2019

In this paper, we propose a system for processing and analyzing drone image information which can be applied variously in disasters-security situation. The proposed system stores the images acquired from the drones in the server, and performs image processing and analysis according to various scenarios. According to each mission, deep-learning method is used to construct an image analysis system in the images acquired by the drone. Experiments confirm that it can be applied to traffic volume measurement, suspect and vehicle tracking, survivor identification and maritime missions.

• 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.

• Journal of the Semiconductor & Display Technology
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• v.10 no.2
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• pp.31-38
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• 2011

This paper introduces a video based traffic monitoring system for detecting vehicles and obstacles on the road. To segment moving objects from image sequence, we adopt the background subtraction algorithm based on the local binary patterns (LBP). Recently, LBP based texture analysis techniques are becoming popular tools for various machine vision applications such as face recognition, object classification and so on. In this paper, we adopt an extension of LBP, called the Diagonal LBP (DLBP), to handle the background subtraction problem arise in vision-based autonomous parking systems. It reduces the code length of LBP by half and improves the computation complexity drastically. An edge based shadow removal and blob merging procedure are also applied to the foreground blobs, and a pose estimation technique is utilized for calculating the position and heading angle of the moving object precisely. Experimental results revealed that our system works well for real-time vehicle localization and tracking applications.