• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.1
• /
• pp.169-174
• /
• 2018

There are many CCTV cameras connected to the video surveillance and monitoring center for the safety of citizens, and it is difficult for a few monitoring agents to monitor many channels of videos. In this paper, we propose an intelligent video surveillance system utilizing a safety map to efficiently monitor many channels of CCTV camera videos. The safety map establishes the frequency of crime occurrence as a database, expresses the degree of crime risk and makes it possible for agents of the video surveillance center to pay attention when a woman enters the crime risk area. The proposed gender classification method is processed in the order of pedestrian detection, tracking and classification with deep training. The pedestrian detection and tracking uses Adaboost algorithm and probabilistic data association filter, respectively. In order to classify the gender of the pedestrian, relatively simple AlexNet is applied to determine gender. Experimental results show that the proposed gender classification method is more effective than the conventional algorithm. In addition, the results of implementation of intelligent video security system combined with safety map are introduced.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.6
• /
• pp.2246-2255
• /
• 2010

In this paper, we describe an ontology-based context aware model that collects context information from USN sensor and CCTV image and reasons about context in order to development an integrated security control system in the industrial environments. The context model represents autonomous and heterogeneous data as ontologies and recognizes the context through DL(description logic) inference in the smart computing environment. We expect that the integrated security control system can automatically detects the risk in the industrial field and reduces the safety and security incidents by applying this context model to the system.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.17 no.4
• /
• pp.137-142
• /
• 2017

Recently, along with the development of IoT technology, technologies for wirelessly sensing various data, such as sensor nodes, RFID, CCTV, smart phones, etc., have rapidly developed, and in the field of multiple applications, to utilize sensor network related technology Have been actively pursued in various fields. Therefore, as GeoSensor utilization increases, query processing systems for efficiently processing 2D data such as spatial sensor data are actively researched. However, existing spatial query processing systems do not support a spatial-temporal data type and a spatial-temporal operator for processing spatial-temporal sensor data. Therefore, they are inadequate for processing spatial-temporal sensor data like GeoSensor. Accordingly, this paper developed a spatial-temporal query processing system, for efficient spatial-temporal query processing of spatial-temporal sensor data in a sensor network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2007.10a
• /
• pp.929-932
• /
• 2007

The integrated prevention of disaster management system is collected prevention of disaster data from prevention of disaster relation other systems and smart sensor in USN. This system manages fire fighting facility effectively. The relation equipment which is used in existing and network using "Open Protocols" about under using the support system which is integrated effectively as the destroyer. It connects CCTV, the sensitivity environmental sensor, automatic fire detection equipment and security equipment and air flow equipment system using Internet. The System Server was collected monitoring data at the each equipment and processing by operational scenario. It will verified the effectiveness of operational scenario and integrated prevention of disaster management system

• The Journal of the Korea institute of electronic communication sciences
• /
• v.10 no.2
• /
• pp.239-246
• /
• 2015

Video based object detection is basic technology of implementing smart CCTV system. Various features and algorithms are developed to detect object, however computations of them increase with the performance. In this paper, performances of object detection algorithms with GPU and CPU are compared. Adaboost and SVM algorithm which are widely used to detect pedestrian detection are implemented with CPU and GPU, and speeds of detection processing are compared for the same video. As results of frame rate comparison of Adaboost and SVM algorithm, it is shown that the frame rate with GPU is faster than CPU.

• Smart Structures and Systems
• /
• v.28 no.6
• /
• pp.799-810
• /
• 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 Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.434-436
• /
• 2022

For 4th industrial revolution and the development of various communication media, unmanned and automation are rapidly progressing in various fields. In particular, high-level image processing technology is required in fields such as smart factories, autonomous driving technology, and intelligent CCTV. Accordingly, the importance of preprocessing in a system operating based on an image is increasing, and an algorithm for effectively removing noise from an image is attracting attention. In this paper, we propose a filtering algorithm using noise judgment and a segmentation mask in a complex noise environment. The proposed algorithm calculates the final output by switching the segmentation mask suitable for filtering by performing noise judgment on the pixel values of the input image. Simulation was conducted to verify the performance of the proposed algorithm, and the result image was compared and evaluated with the existing filter algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.133-135
• /
• 2022

With the development of IoT and AI technologies and media, various digital devices are being used, and unmanned and automation is progressing rapidly. In particular, high-level image processing technology is required in fields such as smart factories, autonomous driving technology, and intelligent CCTV. However, noise present in the image affects processes such as edge detection and object recognition, and causes deterioration of system accuracy and reliability. In this paper, we propose a filtering algorithm using fuzzy weights to reconstruct images damaged by complex noise. The proposed algorithm obtains a reference value using noise judgment and calculates the final output by applying a fuzzy weight. Simulation was conducted to verify the performance of the proposed algorithm, and the result image was compared with the existing filter algorithm and evaluated.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2020.07a
• /
• pp.667-668
• /
• 2020

최근에는 범죄예방과 시설물 안전 및 출입관리, 화제예방 등 다양한 목적으로 감시카메라(CCTV)가 널리 보급되어 공공기관이나 상업시설 등 어느 곳에서나 찾을 수 있다. 이렇듯 많은곳에서 저마다의 이유로 사용되고 있는 감시카메라지만, 가장 흔하게 사용되는 이유는 범죄예방에 있다. 시설 내·외부에 감시카메라를 설치하여 도난사건 등을 사전에 방지하고 사후 증거확보에 유용하게 쓰일 수 있기 때문인데 문제는 감시카메라를 통해 사건을 사전에 방지하기 위해서는 실시간으로 사람이 카메라 영상을 지켜보고 있어야 한다. 하지만 사람이 항상 감시할수있을수는 없기 때문에 예방보다는 사후 증거확보를 위해 사용되는 실정이다. 즉 대부분의 감시카메라가 본래의 역할에 절반밖에 하지 못하고 있다는 것이다. 본 과제는 이러한 문제점을 해결하기 위해 감시카메라 자체의 기능을 추가하여 감시카메라가 인력을 필요로 하지않고 스스로 본래의 역할을 다할수있게 할 것이다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.11a
• /
• pp.1071-1074
• /
• 2021

본 논문은 펌웨어와 인공지능을 이용하여 지형의 사각지대를 이동하며 순찰 및 방범의 목적을 지닌 시스템을 소개하기 위함에 있다. 기존의 보안 시스템은 비상 상황 발생 시 인력이 직접 출동하여 상황을 해결함으로써 날로 증가하는 최저임금을 고려했을 때 이들의 인건비를 감당하기 어렵다는 단점이 있다. [1] 이러한 문제점을 해결하기 위해 앱 개발을 통해 RC카를 제어하는 아두이노와 연결하여 자율주행을 하게끔 하는 시스템을 개발했다. 또한, 라즈베리파이 웹캠을 부착해 실시간으로 현장을 촬영하여 사용자가 웹에만 접속하면 현장을 모두 감시할 수 있도록 시스템을 개발하였고, 단시간 푸리에 변환(STFT)을 통해 얻은 음성 데이터 변환맵을 인공지능 프로세서인 인텔리노에 학습 데이터로 학습시킨 후에 주변 환경에서 비명 소리만 감지할 수 있도록 시스템을 구현하였다. 본 논문에서는 이러한 시스템들이 기존의 인건비 증가에 대한 문제점을 해소할 수 있다고 생각하여 더욱 효율적으로 방범이 가능한 시스템을 소개한다.