• Smart Media Journal
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• v.13 no.2
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• pp.68-84
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• 2024

This paper describes the development of a system and algorithms for high-quality welfare services by recognizing behavior development indicators (activity, sociability, danger) in children aged 0 to 2 years old using action recognition technology. Action recognition targeted 11 behaviors from lying down in 0-year-olds to jumping in 2-year-olds, using data directly obtained from actual videos provided for research purposes by three nurseries in the Gwangju and Jeonnam regions. A dataset of 1,867 actions from 425 clip videos was built for these 11 behaviors, achieving an average recognition accuracy of 97.4%. Additionally, for real-world application, the Edge Video Analyzer (EVA), a behavior analysis device, was developed and implemented with a region-specific random frame selection-based PoseC3D algorithm, capable of recognizing actions in real-time for up to 30 people in four-channel videos. The developed system was installed in three nurseries, tested by ten childcare teachers over a month, and evaluated through surveys, resulting in a perceived accuracy of 91 points and a service satisfaction score of 94 points.

• Journal of Environmental Science International
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• v.25 no.6
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• pp.799-815
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• 2016

In this study, according to the reference setting based on the runoff video of 9:00 where the highest water level of 3.94 m has been recorded during the runoff of Cheon-mi Stream in Jeju Island by the attack of Typhoon no. 16 Sanba on September $17^{th}$, 2012, the error rate of long-distance and short-distance velocimetry and real-distance change rate by input error have been calculated and the input range value of reference point by stream has been suggested. In the reference setting process, if a long-distance reference point input error occurs, the real-distance change rate of 0.35 m in the x-axis direction and 1.35 m in y-axis direction is incurred by the subtle input error of 2~11 pixels, and if a short-distance reference point input error occurs, the real-distance change rate of 0.02 m in the x-axis direction and 0.81 m in y-axis direction is incurred by the subtle input error of 1~11 pixels. According to the long-distance reference point setting variable, the velocity error rate showed the range of fluctuation of at least 14.36% to at most 76.06%, and when calculating flux, it showed a great range of fluctuation of at least 20.48% to at most 78.81%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.5
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• pp.1-12
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• 2013

Road's accidents caused by Ice, snow, Wet of roads surface conditions and weather conditions situations that are constantly occurring. That is, driver's negligence and safe driving ability of individuals due to lack of awareness, and Road management main agent(the government and the public, etc.) due to road conditions, if there is insufficient information. So Related research needs is a trend that is required. In this study, gather Camera(Stereo camera)'s image data, and analysis polarization coefficients and wavelet transform. And unlike traditional single-dimensional classification algorithms as multi-dimensional analysis by using SVM classification techniques, develop an algorithm to determine road conditions. Four on the road conditions (dry, wet, snow, ice) recognition success rate for the detection and analysis of experiments.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.6
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• pp.451-459
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• 2016

Crack detection on panels during manufacturing process is an important step for ensuring the product quality. The accuracy and efficiency of traditional crack detection methods, which are performed by eye inspection, are dependent on human inspectors. Therefore, implementation of an on-line and precise crack detection is required during the panel pressing process. In this paper, a regular CCTV camera system is utilized to obtain images of panel products and an image process based crack detection technique is developed. This technique uses a comparison between the base image and a test image using an amplitude mapping of the local image. Experiments are performed in the laboratory and in the actual manufacturing lines to evaluate the performance of the developed technique. Experimental results indicate that the proposed technique could be used to effectively detect a crack on panels with high speed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1C
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• pp.24-32
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• 2010

In this paper we proposed an object tracking system that detects the abandoned and removed objects, which is to be used in the intelligent surveillance applications. After the GMM based background subtraction and by using histogram method, the static region is identified to detect abandoned and removed objects. Since the system is implemented on DSP chip, it operates in realtime and is programmable. The input videos used in the experiment contain various indoor and outdoor scenes, and they are categorized into three different complexities; low, midium and high. By 10 times of experiment, we obtained high detection ratio at low and medium complexity sequences. On the high complexity video, successful detection ratio was relatively low because the scene contains crowdedness and repeated occlusion. In the future work, these complicated situation should be solved.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.2
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• pp.257-263
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• 2013

In case of image observation equipments, CCTV for short distance visual field is usually installed and operated mostly as the means of crime-prevention. However, the extensive demand for monitoring problems in case of the increase in intelligent crimes and disasters has led to the necessity of the development of long-distance observation equipments embedded with Night View functions. In case of the Night View equipments, the relevant market is set up to be focused mostly on Thermal Observation Device(hereinafter, TOD), but some shortcomings such as the limitation of image visibility and excessive maintenance cost, etc. have actually caused the necessity of new long distance Night View equipment. Moreover there might follow lots of difficulties in long-distance visualization in the event that irregular reflection is generated by minute particles in the atmosphere such as fog, smog, and dust, etc. These factors are motivate the work presented in this study. Our study is aimed at the realization of Pulsed Laser Illuminator and newly proposed Range-Gated image acquisition technology. And also the implementation of Tracker for continuous trace of the objects of interest from the obtained sequence images.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.32-40
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• 2021

Underground information is difficult to visually check, which can lead to a huge accident in the event of a safety accident. Recently, the Ministry of Land, Infrastructure and Transport intends to reduce safety accidents caused by the aging or damage of underground facilities through the Special Act on Underground Safety Management. GPR is increasingly being used as a technology to acquire information in underground spaces that are difficult to see with the naked eye. However, GPR's location information is corrected by checking images of CCTV and GPS information acquired during exploration. This method has an average error of about 2 meters. In this works, We used LiDAR to calibrate the GPR information and found that the error was reduced from at least 7cm to up to 40cm. If accurate GPR information collected in the future is analyzed quickly using AI, etc., it will be able to collect and utilize underground information faster than it is now to secure safety.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.553-564
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• 2012

Today's security initiatives tend to integrate the physical and information securities which have been run by completely separate departments. That is, the converged security management becomes the core in the security market trend. However, to the best of our knowledge, we cannot find any solutions how to combine these two security events for the converged security. In this paper, we propose an information security object-driven approach which utilizes the physical security events to enhance and improve the information security. For scalability, we also present a systematic method using the analytic hierarchy process finding the meaningful event combinations among the large number of physical security events. In particular, we show the whole implementation processes in detail where we consider the information security object 'illegal computing system access' combined with two physical security devices - access controller and CCTV+video analyzer system.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.1
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• pp.1-6
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• 2021

Recently, unmanned facilities have been introduced and operated in a way that reduces the cost and development of IT technology. Although unmanned facilities have advantages in terms of efficiency and economy, they have disadvantages such as failure of unmanned facilities and malfunctions, causing damage to facilities caused by intruders, and information leakage. In addition, it is necessary to visit the person in charge at all times to inspect the unmanned facilities, resulting in management costs. In this paper, we designed a system that checks the status of unmanned surveillance facilities in real time to check and automatically recover problems such as malfunctions, and to notify managers of situations by text messages in real time. The system to be designed consists of an integrated network video server (NVR) that receives and determines information on the operation status of the main equipment such as video, sound, and lighting, and a real-time text message using an SMS server.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1762-1769
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• 2021

The final goal of this study is to develop a system that can analyze whether a wanted vehicle is a criminal vehicle from images collected from black boxes, smartphones, CCTVs, and so on. Data collection was collected using a self-developed black box. The used data in this study has used a total of 83,753 cases such as the eight vehicle types(truck, RV, passenger car, van, SUV, bus, sports car, electric vehicle) and 434 vehicle models. As a result of vehicle recognition using YOLO v5, mAP was found to be 80%. As a result of identifying the vehicle model with ReXNet using the self-developed black box, the accuracy was found to be 99%. The result was verified by surveying field police officers. These results suggest that improving the accuracy of data labeling helps to improve vehicle recognition performance.