• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1966-1972
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• 2011

Optical flow estimation based on multi constraint approaches is frequently used for recognition of moving objects. This paper proposed the method to monitor passenger boarding using image processing when a train is operated based on Automatic Train Operation(ATO). The movement of passenger can be detected to compare two images, one is a basic image and another is immediately captured by CCTV. Optical Flow helps to find the movement of passenger when two images are compared. The movement of passenger is one of important informations for ATO system because it needs to decide door status.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.52 no.1
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• pp.56-64
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• 2016

This study analyzed characteristics of officers' watch-keeping during fishing operation at the fisheries training ship KAYA (GT: 1,737 tons, Pukyong National University). It observed fishing works of three officers in wheel house of KAYA. The observations were carried out at the fishing ground 45 miles away from east of Jeju from 7 to 8 January 2010. The works and movements of the officers were recorded with three common video cameras and a 4-channel MPEG-4 Triplex DVR. Recorded data of the working circulation was analyzed by using the post-processing method. As a result of the traffic lines, the average (${\pm}S.D$) of working hour (min) and moving frequency (times), distance (m) and speed (m/min) during setting the net was 11.8 (0.9), 43.7 (8.1), 133.9 (35.8) and 10.5 (0.6), respectively. During trawling the net, it was 100, 241 (39.8), 615.7 (194.6) and 5.2 (1.6), respectively. During hauling the net, it was 17.6 (1.4), 41.0 (7.2), 196.9 (37.6) and 10.7 (0.8), respectively. In addition, it has a different tendency of the instrument usage frequency by the fishing works. During setting, the usage priority was CCTV, ECDIS, RPM and pitch controller, net monitor, GPS plotter, chart room, X-band radar, fish finder and public addressor. During trawling, it was CCTV, ECDIS, fish finder, X-band radar, net monitor, chart room, GPS plotter, RPM and pitch controller, auto pilot and steering, interphone, wind speed and direction indicator, No.1. VHF, navigation light control panel and public addressor. During hauling, it was CCTV, RPM and pitch controller, GPS plotter, public addressor, chart room, net monitor, X-band radar, auto pilot and steering and fish finder.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.149-156
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• 2019

The existing subtract image based intrusion detection system for CCTV digital images has a problem that it can not distinguish intruders from moving backgrounds that exist in the natural environment. In this paper, we tried to solve the problems of existing system by designing real - time intrusion detection system for CCTV digital image by combining subtract image based intrusion detection method and background learning artificial neural network technology. Our proposed system consists of three steps: subtract image based intrusion detection, background artificial neural network learning stage, and background artificial neural network evaluation stage. The final intrusion detection result is a combination of result of the subtract image based intrusion detection and the final intrusion detection result of the background artificial neural network. The step of subtract image based intrusion detection is a step of determining the occurrence of intrusion by obtaining a difference image between the background cumulative average image and the current frame image. In the background artificial neural network learning, the background is learned in a situation in which no intrusion occurs, and it is learned by dividing into a detection window unit set by the user. In the background artificial neural network evaluation, the learned background artificial neural network is used to produce background recognition or intrusion detection in the detection window unit. The proposed background learning intrusion detection system is able to detect intrusion more precisely than existing subtract image based intrusion detection system and adaptively execute machine learning on the background so that it can be operated as highly practical intrusion detection system.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.4
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• pp.1988-1992
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• 2013

In this paper, we propose a method of object recognition to real image object which enter into an area. We needs this method for an application module to detect and trace the moving pattern of some objects entered into an specific area. A scheme to the object recognition is adopted to some applied modules that it is moved from only real image information recognition to real coordination recognition, the mapping between the GPS coordination and real image information provides object coordination.

• Proceedings of the KSR Conference
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• 2003.05a
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• pp.468-473
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• 2003

CCTV network has been implemented to transmit the image information of platfo gate and transfer section to local headquarters in the KNR(Korean National Railroa But. communication system for transferring image information around accident field has not been established yet. thus, at present implementation of communication equipment is necessary for dyn of unspecified accident to be transmitted to headquarters. Copper cable communication network is run by KNR, but it is processing installatio optics cable in connection with the implemental plan for high-speed network from n And, the capacity of communication channel will be guaranteed much more than een station and station, station and central headquarters when fiber optics cable is This study analyzes the image equipment of field for transmission and estimated m transmit image information of accident field to headquarters with using communicat ucture. And, the study considers implemental method of communication network for nce image transmission from dozens to hundreds kilometers.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.1300-1302
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• 2003

CCTV network has been implemented to transmit the image information of platform, ticket gate and transfer section to local headquarters in the KNR(Korean National Railroad). But, communication system for transferring image information around accident field or wayside has not been established yet. thus, at present implementation of communication equipment is necessary for dynamic image of unspecified accident to be transmitted to headquarters. Copper cable communication network is run by KNR, but it is processing installation of optical cable in connection with the implemental plan for high-speed network from now on. And, the capacity of communication channel will be guaranteed much more than now between station and station, station and central headquarters when optical cable is completed. This study analyzes the image equipment of field for transmission and estimated matters to transmit image information of accident field to headquarters with using communication infrastructure. And, the study considers implemental method of communication network for long-distance image transmission from dozens to hundreds kilometers.

• Annual Conference of KIPS
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• 2009.04a
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• pp.132-133
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• 2009

움직임 검출 시스템은 감시카메라에서 불필요한 녹화를 방지하는 방법으로 널리 사용되고 있다. 그러나 최근 출시되고 있는 고화질 CCTV 카메라에서는 연산의 복잡도 때문에 실시간 처리가 어려운 실정이다. 이를 해결하기 위해 본 논문에서는 CUDA를 이용한 고속 움직임 탐지 시스템을 구현하였다. 기존의 움직임 탐지 시스템은 처리 속도의 한계로 인해 고속의 탐지가 어려웠을 뿐 아니라 고속으로 동작하도록 하려면 고가의 시스템 부품을 사용하여야 하므로 사용자에게 부담을 안겨주었다. 그러나 최근 발전을 거듭하고 있는 고속의 GPU를 이용하여 움직임 탐지 시스템을 구현할 경우 보다 저렴한 가격에 보다 뛰어난 성능을 가질 수 있도록 할 수 있다. 따라서 본 논문에서는 이러한 범용 GPU 사용기술인 nVidia의 CUDA를 이용하여 움직임 탐지 시스템을 구현하였다. 실험 결과 GPU 기반 시스템은 CPU 기반 시스템보다 80배가량 속도의 향상이 있었다. 제안하는 방법은 nVidia 그래픽 카드가 설치된 시스템에서 고속의 감시카메라 서버 등으로 적용이 가능하다.