• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1081-1088
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• 2020

In this paper, the method of detecting sea fog through CCTV image is proposed based on convolutional neural networks. The study data randomly extracted 1,0004 images, sea-fog and not sea-fog, from a total of 11 ports or beaches (Busan Port, Busan New Port, Pyeongtaek Port, Incheon Port, Gunsan Port, Daesan Port, Mokpo Port, Yeosu Gwangyang Port, Ulsan Port, Pohang Port, and Haeundae Beach) based on 1km of visibility. 80% of the total 1,0004 datasets were extracted and used for learning the convolutional neural network model. The model has 16 convolutional layers and 3 fully connected layers, and a convolutional neural network that performs Softmax classification in the last fully connected layer is used. Model accuracy evaluation was performed using the remaining 20%, and the accuracy evaluation result showed a classification accuracy of about 96%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.69-73
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• 2022

In this paper, we propose a new method for measuring fine dust through a LoG(Laplacian of Gaussian)-based edge detection technique. CCTV-based images in a video are collected for fine dust measurement, and image ranges are designated through RoI(Region of Interest). After clustering by applying the GMM(Gaussian Mix Model) to the specified area, we detect edge through the LoG algorithm and measure the detected edge strength. The concentration of fine dust is determined based on the measured intensity data of the edge. In this paper, we propose algorithm as the effectiveness of experiment. As a result of collecting and applying CCTV image in the video installed around the laboratory of this school for a month from June to July, the measured result value was proved through this experiment to be sufficient to calculate the concentration and range of fine dust.

• Smart Media Journal
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• v.1 no.3
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• pp.43-47
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• 2012

The recent proliferation of multimedia data necessitates the effectively and efficiently retrieving of multimedia data. These research not only focus on the retrieving methods of text matching but also on using the multimedia data features. Therefore, this paper is a similarity measurement method of trajectory using indexing information of moving object in video, for similarity measurement. This method consists of 2 steps. Firstly, Video data is processed indexing for trajectory extraction of moving objects using CCTV. Finally, we describe to compare DTW(Dynamic Time Warping) to TSR(Tansent Space Representation) algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.376-378
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• 2022

Artificial intelligence technology is widely used in various fields such as artificial intelligence speakers, artificial intelligence chatbots, and autonomous vehicles. Among these AI application fields, the image processing field shows various uses such as detecting objects or recognizing objects using artificial intelligence. In this paper, data synthesized by a virtual human is used as a method to analyze images taken in a specific space.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.4
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• pp.124-137
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• 2018

The main purpose of this study is to find the hotspots of crimes that occur frequently in the space and to derive the appropriate CCTV installation location. One of the characteristics of crime is clustered around past occurrence area, and these crimes are strongly correlated. It is also possible to find the cause of the clusters and the variables that affect the crime through the history of the crime. In addition to the traditional OLS model, spatial differential model including spatial autocorrelation and spatial error model were used to select the variables influencing the five major crime rate, the theft rate and the foreign resident rate. The variables affecting the Five major crimes were positive (+) sign for the welfare and the rate of the bar cluster rate, and negative (-) for the street density. The CCTV area occupies 46% of the hotspots based on the overlapping of the areas where the elderly people are crowded, the bar cluster, many multicultural families, and the areas with low density of street lamps. It turned out. Taking into account the current CCTV operation, the total number of new cases to cover the risk point was 89.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.147-157
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• 2022

Recently, many studies have been conducted to analyze traffic or object recognition that classifies vehicles through artificial intelligence-based prediction models using CCTV (Closed Circuit TeleVision)or drone images. In order to develop an object recognition deep learning model for accurate traffic estimation, systematic data construction is required, and related standardized guidelines are insufficient. In this study, previous studies were analyzed to derive guidelines for establishing artificial intelligence-based training data for traffic estimation using drone images, and business reports or training data for artificial intelligence and quality management guidelines were referenced. The guidelines for data construction are divided into data acquisition, preprocessing, and validation, and guidelines for notice and evaluation index for each item are presented. The guidelines for data construction aims to provide assistance in the development of a robust and generalized artificial intelligence model in analyzing the estimation of road traffic based on drone image artificial intelligence.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.1-9
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• 2022

In this paper, we propose the Deep Learning-Based Companion Animal Abnormal Behavior Detection Service, which using video and sensor data. Due to the recent increase in households with companion animals, the pet tech industry with artificial intelligence is growing in the existing food and medical-oriented companion animal market. In this study, companion animal behavior was classified and abnormal behavior was detected based on a deep learning model using various data for health management of companion animals through artificial intelligence. Video data and sensor data of companion animals are collected using CCTV and the manufactured pet wearable device, and used as input data for the model. Image data was processed by combining the YOLO(You Only Look Once) model and DeepLabCut for extracting joint coordinates to detect companion animal objects for behavior classification. Also, in order to process sensor data, GAT(Graph Attention Network), which can identify the correlation and characteristics of each sensor, was used.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.143-144
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• 2022

자율운항선박의 원격제어를 위해 선박으로부터 다양한 종류와 포맷의 데이터를 전송받아야 한다. 선체 주변환경을 파악하기 위해 CCTV 영상을, 선박의 운항상 태를 파악하기 위해 각종 센서 메시지를, 레이더신호를 전시하기 위한 레이더 신호 혹은 이미지 등의 데이터를 받아 육상제어 시스템에 전시하고, 사용자의 제어명령을 선박에 전달하기 위한 메시지 교환시스템을 개발하였다.

• 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.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.9-16
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• 2009

In ubiquitous spatial computing environments, GeoSensor generates sensor data streams including spatial information as well as various conventional sensor data from RFID, WSN, Web CAM, Digital Camera, CCTV, and Telematics units. This GeoSensor enables the revitalization of various ubiquitous USN technologies and services on geographic information. In order to service the u-GIS applications based on GeoSensors, it is indispensable to efficiently process sensor data streams from GeoSensors of a wide area. In this paper, we propose a GeoSensor data stream processing system for u-GIS computing over real-time stream data from GeoSensors with geographic information. The proposed system provides efficient gathering, storing, and continuous query processing of GeoSensor data stream, and also makes it possible to develop diverse u-GIS applications meet each user requirements effectively.