• Journal of Digital Convergence
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• v.11 no.10
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• pp.359-366
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• 2013

Due to the development and market expansion of image analysis and recognition technology, video security such as CCTV cameras and digital storage devices, are required for real-time monitoring systems and intelligent video security systems. This includes the development of more advanced technologies. A rotatable PTZ camera, in a CCTV camera system, has a zoom function so you can acquire a precise picture. However it can cause blind spots, and can not monitor two or more moving objects at the same time. This study concerns, the intelligent tracking of multiple moving objects, CCTV systems, and methods of video surveillance. An intelligent video surveillance system is proposed. It can accurately shoot broad areas and track multiple objects at the same time, much more effectively than using one fixed camera for an entire area or two or more PTZ cameras.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.519-529
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• 2013

This paper introduces a moving object tracing system using network-connected CCTV and smartphone. In an emergency situation, a smartphone of the reporter gains its GPS information and sends that to the central server. The central server stores received GPS information as a dangerous area in the database and keeps sending the order of tracing the reporter to a network-connected CCTV which can film the reporter. At the same time, the central server sends pictures of the reporter to his or her family and related organization in order to handle the emergency situation as soon as possible. In addition, when a reporter want to know the risk around destination, the central server informs dangerous areas to the reporter by using smart phone application and database of a danger spot.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.295-296
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• 2006

This paper implements a method that tracking the moving objects that detected by the motion detection function of the digital CCTV system. We simply implement the motion detection function of the digital CCTV system that use frame difference and thresholding. When motion is detected, the motion detection function generates two outputs. One output is the event that the motion is arised in input image frame. The other output is coordinate that motion is exists. Then, do the block matching algorithm[2] using coordinate, that motion is exists, as initial coordinate of the block matching algorithm. The best matched coordinate is new initial coordinate of the block matching algorithm for the next image frame. We simply use the block matching algorithm that implements tracking the moving objects. It is simple, but useful the actual digital CCTV system.

• Journal of Korea Multimedia Society
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• v.19 no.4
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• pp.681-688
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• 2016

In the intelligent video surveillance, recognizing the moving objects is important issue. However, the conventional moving object recognition methods have some problems, that is, the influence of light, the distinguishing between similar colors, and so on. The recognition methods for the moving objects using depth information have been also studied, but these methods have limit of accuracy because the depth camera cannot measure the depth value accurately. In this paper, we propose a recognition method for the moving objects by using both the depth and the color information. The depth information is used for extracting areas of moving object and then the color information for correcting the extracted areas. Through tests with typical videos including moving objects, we confirmed that the proposed method could extract areas of moving objects more accurately than a method using only one of two information. The proposed method can be not only used in CCTV field, but also used in other fields of recognizing moving objects.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2575-2580
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• 2013

In this paper, we watch out key measure of the level of transportation service about travel time and delay time. And we measured vehicle traffic by moving vehicle method using CCTV which is one of the travel time measure. We should be measured in place of continuous traffic flow characteristics with wide traffic light interval. In addition, traffic flow on the other side of the interval must be sufficiently identifiable and at the end of this section must be possible U-turn. This method it requires only the driver of the vehicle because of the CCTV measure. In addition, We cannot require time, distance, and traffic equipment that can be recorded. Because equipped with the software to do that. In addition to traffic, average travel time, average space speed, traffic density are also available.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.97-105
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• 2020

Recently, for a smart city, there is a demand for a technology for acquiring traffic information using an intelligent road infrastructure and managing it. In the meantime, various technologies such as loop detectors, ultrasonic detectors, and image detectors have been used to analyze road traffic information but these have difficulty in collecting various informations, such as traffic density and length of a queue required for building a traffic information DB for moving vehicles. Therefore, in this paper, assuming a smart city built on the basis of a camera infrastructure such as intelligent CCTV on the road, a solution for calculating the traffic DB of moving vehicles using Vision Tracking of road CCTV cameras is presented. Simulation and verification of basic performance were conducted and solution can be usefully utilized in related fields as a new intelligent traffic DB calculation solution that reflects the environment of road-mounted CCTV cameras and moving vehicles in a variable smart city road environment. It is expected to be there.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.3
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• pp.247-262
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• 2022

In domestic tunnels, it is mandatory to install CCTVs in tunnels longer than 200 m which are also recommended by installation of a CCTV-based automatic accident detection system. In general, the CCTVs in the tunnel are installed at a low height as well as near by the moving vehicles due to the spatial limitation of tunnel structure, so a severe perspective effect takes place in the distance of installed CCTV and moving vehicles. Because of this effect, conventional CCTV-based accident detection systems in tunnel are known in general to be very hard to achieve the performance in detection of unexpected accidents such as stop or reversely moving vehicles, person on the road and fires, especially far from 100 m. Therefore, in this study, the region of interest is set up and a new concept of inverse perspective transformation technique is introduced. Since moving vehicles in the transformed image is enlarged proportionally to the distance from CCTV, it is possible to achieve consistency in object detection and identification of actual speed of moving vehicles in distance. To show this aspect, two datasets in the same conditions are composed with the original and the transformed images of CCTV in tunnel, respectively. A comparison of variation of appearance speed and size of moving vehicles in distance are made. Then, the performances of the object detection in distance are compared with respect to the both trained deep-learning models. As a result, the model case with the transformed images are able to achieve consistent performance in object and accident detections in distance even by 200 m.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.155-158
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• 2019

최근 지능형 CCTV 관제 시스템에 대한 수요가 증가하고 있다. CCTV 영상 데이터의 양이 폭발적으로 증가하고 있어 이를 분석하기 위한 기술의 발전이 필요한 실정이다. 대부분의 지능형 CCTV 관제 시스템은 영상 속 객체를 찾고 이 객체의 메타데이터를 통해 지능형 관제 시스템을 수행한다. 하지만 영상 속 객체의 로그가 항상 정확하지 않다. 현재의 객체 인식 기술로는 CCTV 영상의 밝기, 해상도 조건에 따라 성능의 차이가 심하고, 영상의 프레임 대비 빠르게 움직인 CCTV 영상 속 모든 객체를 사람이 인식하는 정도로 인식하기 어렵다. 이러한 이동 객체의 크기, 위치를 분석한 메타데이터에는 에러가 포함되기 쉽다. 본 논문에서는 지능형 CCTV 관제 시스템에서 분석한 영상 속 객체의 프레임 메타데이터 에러를 학습기반 실시간 에러 필터링 알고리즘을 통해 개선하여 에러가 필터링된 데이터를 사용하는 지능형 관제 시스템의 정확도 향상에 기여 할 것을 기대한다.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2007.05a
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• pp.1-8
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• 2007

본 논문은 저해상도와 많은 노이즈를 갖는 일반 CCTV의 입력 영상에서 실시간으로 움직이는 물체를 검출하고 그 물체의 움직임을 추적하는 방법을 제안 한다. 본 논문은 CCTV영상으로부터의 입력 영상을 순차를 갖는 명암도 영상으로 실시간 변환 하여 진행 한다. 움직이는 물체의 추출은 첫째, 획득한 영상의 그레이 영상을 포스터라이징을 이용하여 명암 분포를 축소하고 차영상을 통해 윤곽을 추출한다. 둘째, 본 논문이 제안하는 영역 단위 이진화를 통해 이진화와 잡음의 제거를 동시에 수행한다. 셋째, 손실된 정보의 보정을 위해 이진 영상의 팽창을 수행한다. 넷째, 이진 영상의 가로/세로 명암 밀도 분포를 통해 움직이는 물체 영역을 검출한다. 검출된 물체의 추적은 현 재 프레임의 물체 영역과 이전 프레임의 물체 영역의 중심을 계산한 후, 두 중심의 거리 차를 계산한다. 계산된 거리가 임계값보다 작을 경우 같은 물체로 인식하고 계속 추적하며, 임계값 이상의 값일 경우 새로운 물체로 인식한다. 추적된 이동물체의 중심점이 화면의 중앙 부분에 있지 않을 경우, 이동물체의 중심으로 카메라의 방향을 조정한다. 실험결과, 제안한 방법으로 저해상도와 많은 노이즈를 갖는 일반 CCTV 의 입력 영상에서도 실시간으로 움직이는 물체를 검출하고, 그 물체의 움직임을 추적 할 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.214-215
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• 2013

The main criteria for objective measure of the level of transportation service is travel time and delay time. In this paper, the level of service is measured by using moving vehicles method. Through this, we get the hourly traffic volume, the average travel time, space average speed, etc. In addition, we get the density of traffic.