• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.450-453
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• 2011

고화질의 디지털 카메라 및 스마트폰, 감시용 카메라의 보급 등으로 인해 최근 패턴 인식 및 이미지 프로세싱 분야에서 고화질의 이미지 및 비디오를 처리해야 하는 경우가 많아지고 있다. 특히 차량 번호판 감지 등과 같은 객체 인식 분야의 경우, 고화질의 이미지로 인해 그만큼 인식에 필요한 계산 비용이 증가하게 되었는데 따라서 이러한 계산 비용을 효율적으로 줄이기 위한 기법이 요구되고 있다. 또한 기존의 차량 번호판 감지의 도메인과는 다르게 도로 상에서의 실시간 차량 번호판 감지의 필요성이 대두되고 있기에 본 논문에서는 도로 상에서의 실시간 번호판 감지 시스템을 위한 차량 번호판 주변정보 기반의 효율적인 윈도우 슬라이딩(window sliding) 방법을 제안한다. 본 논문의 시스템은 총 3단계로, (1) SVM(Supported Vector Machine) 을 통한 차량 번호판 주위 정보에 대한 학습, (2) 도로 상의 번호판 위치 확률 모델링을 통한 탐색 공간의 감소, (3) $context_{plate}$분류기를 통한 OCS(operator context scanning)의 수행이다. 이와 같은 $context_{plate}$분류기와 OCS를 통해 번호판 검출을 위한 윈도우 슬라이딩의 수가 크게 줄었음을 알 수 있었으며, 또한 번호판의 정보를 건너뛰지 않고, 신뢰성 있게 접근함을 알 수 있었다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.373-376
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• 2019

최근 AI 기술이 학술적 연구단계를 넘어 산업계에서 활용이 확산됨에 따라, 실시간 시스템 분야에서도 AI를 접목하려는 움직임이 증가하고 있다. 실시간 시스템은 시간 제약이 있는 시스템으로써, 자율주행 시스템과 같은 안전 최우선 시스템에서는 시간 제약 위반으로 인한 예외 상황 발생 시, 심각한 피해로 이어질 수 있기 때문에 예외 상황 처리를 위한 신뢰성 있는 시스템 설계가 필요하다. 본 논문에서는 실시간 임베디드 AI를 위한 스트림 데이터 처리 언어인 Splash에 신뢰성 향상을 위한 예외 처리 메커니즘을 구현하였다. 구체적으로 예외 감지와 처리 부분으로 나눠서, 예외 상황을 감지하는 시점 및 방법을 정의하고 예외 발생 시 이를 처리하는 과정을 구현하였다. 예외 처리 메커니즘은 실시간 발행/구독 기반의 통신 미들웨어인 DDS 상에 라이브러리 형태로 구현되었으며, 시계열 센서 데이터를 사용하여 확인한 결과, 응용 개발자가 명시한 제약 사항 위반 시 발생하는 예외를 성공적으로 감지하고 예외 처리 동작을 수행하는 것을 확인하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1082-1087
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• 2019

Video Incident Detection System is a detection system for the purpose of detection of an emergency in an unexpected situation such as a pedestrian in a tunnel, a falling object, a stationary vehicle, a reverse run, and a fire(smoke and flame). In recent years, the importance of the city center has been emphasized by the construction of underpasses in great depth underground space. Therefore, in order to apply Video Incident Detection System to a Double Deck Tunnel, it was developed to reflect the design characteristics of the Double Deck Tunnel. and In this paper especially, the fire detection technology, which is not it is difficult to apply to the Double Deck Tunnel environment because it is not supported on existing Video Incident Detection System or has a fail detect, we propose fire detection using color image analysis, silhouette spread, and statistical properties, It is verified through a real fire test in a double deck tunnel test bed environment.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.364-373
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• 2022

Purpose: The purpose of this paper is to develope smoke detection using AI model for detecting the initial fire in underground utility tunnels using CCTV Method: To improve detection performance of smoke which is high irregular, a deep learning model for fire detection was trained to optimize smoke detection. Also, several approaches such as dataset cleansing and gradient exploding release were applied to enhance model, and compared with results of those. Result: Results show the proposed approaches can improve the model performance, and the final model has good prediction capability according to several indexes such as mAP. However, the final model has low false negative but high false positive capacities. Conclusion: The present model can apply to smoke detection in underground utility tunnel, fixing the defect by linking between the model and the utility tunnel control system.

• Journal of Internet Computing and Services
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• v.13 no.1
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• pp.7-14
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• 2012

In this paper, performance of IEEE 802.11 carrier-sense multiple access with collision-avoidance (CSMA/CA) protocols in saturated traffic conditions is presented taking into account the impact of imperfect channel sensing. The imperfect channel sensing includes both missed-detection and false alarm and their impact on the performance of IEEE 802.11 is analyzed and expressed as a closed form. To include the imperfect channel sensing at the physical layer, we modified the state transition probabilities of well-known two state Markov process model. Simulation results closely match the theoretical expressions confirming the effectiveness of the proposed model. Based on both theoretical and simulated results, the probability of detection is concluded as a dominant factor for the performance of IEEE 802.11.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.393-395
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• 2013

In modern society, the elderly over 65 years old are increasing due to development of medical technology and improvement of their standard of living. Severe fall of the elderly can lead to death threats. To solve this problem, several algorithms and hardware systems for fall detection have been studied and developed. In this paper, a fall detection system using 3-axial acceleration sensor is presented. In the fall detection system, several types of fall-feature parameters are calculated and then the fall is determined by using them. Using this system, best sensitivity and specificity are 98.3% and 94.7%, respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.31-33
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• 2021

In this paper, a dataset was designed and built to improve the accuracy of finger direction detection using an object detection algorithm based on You Only Look Once (YOLO). In order to improve the object detection performance, about 200 finger image data sets were trained, and to confirm that the detection accuracy differs from each other according to the angle of the palm, 50 comparison groups of different angles were configured and tested. As a result of the experiment, it was confirmed that the detection accuracy of palm located in a direction close to 90° is higher than that of other angles.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.87-94
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• 2008

In this paper, we propose a new vision-based fire detection method for a real-life application. Most previous vision-based methods using color information and temporal variation of pixel produce frequent false alarms because they used a lot of heuristic features. Furthermore there is also computation delay for accurate fire detection. To overcome these problems, we first detected candidated fire regions by using background modeling and color model of fire. Then we made probabilistic models of fire by using a fact that fire pixel values of consecutive frames are changed constantly and applied them to a Bayesian Network. In this paper we used two level Bayesian network, which contains the intermediate nodes and uses four skewnesses for evidence at each node. Skewness of R normalized with intensity and skewnesses of three high frequency components obtained through wavelet transform. The proposed system has been successfully applied to many fire detection tasks in real world environment and distinguishes fire from moving objects having fire color.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.93-96
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• 2015

In this paper, we introduce a fire detection system using context-aware sensor. In existing weather and based on vision sensor of fire detection system case, acquired image through sensor of camera is extracting features about fire range as processing to convert HSI(Hue, Saturation, Intensity) model HSI which is color space can have durability in illumination changes. However, in this case, until a fire occurs wide range of sensing a fire in a single camera sensor, it is difficult to detect the occurrence of a fire. Additionally, the fire detection in complex situations as well as difficult to separate continuous boundary is set for the required area is difficult. In this paper, we propose an algorithm for real-time by using a temperature sensor, humidity, Co2, the flame presence information acquired and comparing the data based on multiple conditions, analyze and determine the weighting according to fire it. In addition, it is possible to differential management to intensive fire detection is required zone dividing the state of fire.

• Journal of the Korea Society of Computer and Information
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• v.15 no.5
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• pp.57-65
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• 2010

In this paper, we propose a new real-time algorithm detecting the flame and smoke in digital CCTV images. Because the forest fire causes the enormous human life and damage of property, the early management according to the early sensing is very important. The proposed algorithm for monitoring forest fire is classified into the flame sensing and detection of smoke. The flame sensing algorithm detects a flame through the conditional test at YCbCr color model from the single frame. For the detection of smoke, firstly the background range is set by using differences between current picture and the average picture among the adjacent frames in the weighted value, and the pixels which get out of this range and have a gray-scale are detected in the smoke area. Because the proposed flame sensing algorithm is stronger than the existing algorithms in the change of the illuminance according to the quantity of sunshine, and the smoke detection algorithm senses the pixel of a gray-scale with the smoke considering the amount of change for unit time, the effective early forest fire detection is possible. The experimental results indicate that the proposed algorithm provides better performance than existing algorithms.