• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.316-318
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• 2019

본 논문은 5G 기반의 스마트 시티를 위한 지능형 도로 안전 지원 서비스의 설계에 관한 것이다. 초저지연, 대용량, 초연결의 특성을 가지는 5G 무선 통신망은 스마트시티를 구현하기 위한 최적의 네트워크 인프라를 제공한다. 본 논문에서는 5G 기반의 스마트 시티 서비스를 제공하기 위한 에지 클라우드 컴퓨팅 인프라를 설계하고, 5G 무선 통신 기반의 지능형 CCTV 로부터 생산되는 대용량의 영상 데이터를 전송, 저장하기 위한 계층형 분산 VMS(Video Management System)의 모델을 제시하고 이를 이용하여 5G 기반의 무선 CCTV 와 디지털 투사, 재현 장치를 포함하는 스마트 가로등을 이용하여 지능형 도로 안전 지원 서비스를 제공하는 방법에 대하여 설명한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.463-466
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• 2011

증강현실이 주목받고 있는 시점에서 현실세계를 3차원으로 가상현실화 하고자 하는 연구가 최근 활발히 진행되고 있다. 현실세계를 CCTV카메라에 담기 위해서는 넓은 시야각을 가진 렌즈가 필요한데, 대안으로써 보통 어안렌즈를 많이 사용한다. 어안렌즈는 시야각을 넓게 하여 정해진 범위 내에 많은 양의 데이터를 활용할 수 있기 때문에 CCTV를 이용한 감시 시스템에 사용된다. 그러나 어안렌즈는 직선성분을 왜곡하여 곡선성분으로 나타내는 경향이 발생하며, 심할 경우 대상을 식별하기가 힘들 수도 있다는 문제점이 있다. 이러한 문제점을 해결하기 위해 원 영상이 왜곡된 영상보다 직선성분이 많다는 것에 착안하여 Hough Transform에 의한 직선검출을 이용해 왜곡을 자동으로 보정하고, Calibration 파라미터를 산출하는 기법을 본 논문에서 제안하고, 실험을 통해 확인하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.3-4
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• 2021

폐쇄된 터널 내부에서는 사고가 일어날 경우 외부에서는 터널 내 상황을 알 수가 없어 경미한 사고라 하더라도 대형 후속 2차 사고로 이어질 가능성이 크다. 또한영상탐지로사고 상황의 오검출을 줄이기 위해서, 본 연구에서는기존의 많은 CNN 모델 중 보유한 데이터에 가장 적합한 모델을 선택하는 과정에서 가장 좋은 성능을 보인 VGG16 모델을 전이학습 시키고 fully connected layer의 일부 layer에 Dropout을 적용시켜 Overfitting을일부방지하는 CNN 모델을 생성한 뒤Yolo를 이용한 영상 내 객체인식, OpenCV를 이용한 영상 프레임 내에서 객체의ROI를 추출하고이를 CNN 모델과 비교하여오검출을 줄이면서 사고를 검출하는 시스템을 제안하였다.

• Journal of Internet Computing and Services
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• v.18 no.4
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• pp.61-67
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• 2017

Recently, CCTV has been combined with areas such as big data, artificial intelligence, and image analysis to detect various abnormal behaviors and to detect and analyze the overall situation of objects such as people. Image analysis research for this intelligent video surveillance function is progressing actively. However, CCTV images using 2D information generally have limitations such as object misrecognition due to lack of topological information. This problem can be solved by adding the depth information of the object created by using two cameras to the image. In this paper, we perform background modeling using Mixture of Gaussian technique and detect whether there are moving objects by segmenting the foreground from the modeled background. In order to perform the depth information-based segmentation using the RGB information-based segmentation results, stereo-based depth maps are generated using two cameras. Next, the RGB-based segmented region is set as a domain for extracting depth information, and depth-based segmentation is performed within the domain. In order to detect the center point of a robustly segmented object and to track the direction, the movement of the object is tracked by applying the CAMShift technique, which is the most basic object tracking method. From the experiments, we prove the efficiency of the proposed object detection and tracking method using the RGB-D model.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.129-135
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• 2015

The Ubiquitous City (U-City) spatial information technology aimed to provide services freely anytime and anywhere by converging high-tech information & communication technology in urban infrastructure has been available in diverse patterns. In particular, there have been studies on the development of 3D spatial information after selecting and matching key points with stereo images from the many Closed Circuit TV (CCTV) in the U-City. However, the data mostly used in extracting matching points haven't considered external environmental impacts such as illuminance. This study tested how much the matching points needed to construct 3D spatial information with the CCTV whose image quality is dependent upon changes in illuminance fluctuate under the same hardware performances. According to analysis on the number of matching points by illuminance, the number of matching points increased up to 3,000Lux in proportion to the illuminance when IRIS, shutter speed and ISO were fixed. In addition, a border between an object and background became more distinctive. When there was too much light, however, the page became brighter, and noise occurred. Furthermore, it was difficult to name key points because of the collapse of an inter-object border. It appears that if filmed with the study results, the number of matching points would increase.

• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.103-113
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• 2019

License plate recognition camera is dedicated device designed for acquiring images of the target vehicle for recognizing letters and numbers in a license plate. Mostly, it is used as a part of the system combined with server and image analysis module rather than as a single use. However, building a system for vehicle license plate recognition is costly because it is required to construct a facility with a server providing the management and analysis of the captured images and an image analysis module providing the extraction of numbers and characters and recognition of the vehicle's plate. In this study, we would like to develop an embedded type convergent camera (Edge Base) which can expand the function of the camera to not only the license plate recognition but also the security CCTV function together and to perform two functions within the camera. This embedded type convergence camera equipped with a high resolution 4K IP camera for clear image acquisition and fast data transmission extracted license plate area by applying YOLO, a deep learning software for multi object recognition based on open source neural network algorithm and detected number and characters of the plate and verified the detection accuracy and recognition accuracy and confirmed that this camera can perform CCTV security function and vehicle number plate recognition function successfully.

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.598-605
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• 2018

Behavior awareness is a technology that recognizes human behavior through data and can be used in applications such as risk behavior through video surveillance systems. Conventional behavior recognition algorithms have been performed using the 2D camera image device or multi-mode sensor or multi-view or 3D equipment. When two-dimensional data was used, the recognition rate was low in the behavior recognition of the three-dimensional space, and other methods were difficult due to the complicated equipment configuration and the expensive additional equipment. In this paper, we propose a method of recognizing human behavior using only CCTV images without additional equipment using only RGB and depth information. First, the skeleton extraction algorithm is applied to extract points of joints and body parts. We apply the equations to transform the vector including the displacement vector and the relational vector, and study the continuous vector data through the RNN model. As a result of applying the learned model to various data sets and confirming the accuracy of the behavior recognition, the performance similar to that of the existing algorithm using the 3D information can be verified only by the 2D information.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.97-113
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• 2023

Swift and appropriate responses in emergency situations like objects falling on the road can bring convenience to road users and effectively reduces secondary traffic accidents. In Korea, current intelligent transportation system (ITS)-based detection systems for emergency road situations mainly rely on loop detectors and CCTV cameras, which only capture road data within detection range of the equipment. Therefore, a new detection method is needed to identify emergency situations in spatially shaded areas that existing ITS detection systems cannot reach. In this study, we propose a ResNet-based algorithm that detects and classifies emergency situations from vehicle camera footage. We collected front-view driving videos recorded on Korean highways, labeling each video by defining the type of emergency, and training the proposed algorithm with the data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.197-205
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• 2024

Development of AI and big data-based algorithms to advance and optimize the recognition and detection performance of various static/dynamic vehicles in front and around the vehicle at a time when securing driving safety is the most important point in the development and commercialization of autonomous vehicles. etc. are being studied. However, there are many research cases for recognizing the same vehicle by using the unique advantages of radar and camera, but deep learning image processing technology is not used, or only a short distance is detected as the same target due to radar performance problems. Therefore, there is a need for a convergence-based vehicle recognition method that configures a dataset that can be collected from radar equipment and camera equipment, calculates the error of the dataset, and recognizes it as the same target. In this paper, we aim to develop a technology that can link location information according to the installation location because data errors occur because it is judged as the same object depending on the installation location of the radar and CCTV (video).

• 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.