• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.2
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• pp.240-244
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• 2007

Non-intrusive methods based on active remote IR illumination fur eye tracking is important for many applications of vision-based man-machine interaction. One problem that has plagued those methods is their sensitivity to lighting condition change. This tends to significantly limit their scope of application. In this paper, we present a new real-time eye detection and tracking methodology that works under variable and realistic lighting conditions. Based on combining the bright-pupil effect resulted from IR light and the conventional appearance-based object recognition technique, our method can robustly track eyes when the pupils are not very bright due to significant external illumination interferences. The appearance model is incorporated in both eyes detection and tracking via the use of support vector machine and the mean shift tracking. Additional improvement is achieved from modifying the image acquisition apparatus including the illuminator and the camera.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.439-447
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• 2000

현재 방범 시스템은 적외선, 초음파 둥의 I-지기의 신호에 의존하여 외부로부터의 침입을 감지하고 있으나 방범 시스템이 설치된 환경에 따라 이런 감지기의 정확성이 달라지므로 오 동작의 가능성을 포함하며 신뢰성이 저하되고 있다. 실제 외부로부터의 침입을 확인하기 위해서는 화상과 음성 그리고 감지기의 신호를 조합하여 정확한 판단이 가능하지만 화상을 전송하기 위해서는 고가의 회선비용을 부담해야만 한다. 하지만 인터넷을 이용하여 화상을 전송하는 제품들이 출시되면서 Real-time 화상감시가 가능해졌다. 이런 화상감시 제품들은 Netscape, MS Explorer와 같은 표준 Web Browser을 통해 Real-time으르 전세계 어디서나 화상을 감시할 수 있다는 장점을 가지고 있다. 적용분야는 호텔주변, 관광지의 여러 명소에 설치 후 홈페이지에 링크시켜 홍보용으로 사용하거나 공장 주요시설, 교통상황 둥의 중요지역의 Monitoring에 활용하고 있다. 그러나 방범 시스템에 적용하기에는 감지기의 확장성, 방범 관제센터 시스템과의 연동이 부족하여 본 고에서는 현재 출시된 화상감시 시스템과 기존 방범시스템을 Integration하여 방범의 최종 목표인 화상과 감지기 신호의 조합에 따른 정확한 방범시스템의 구현에 대해서 기술하고자 한다. 화상방범시스템의 구현은 크게 두 가지로 나누어지며 첫째는 화상감시 시스템과 방범시스템간의 통신을 설계하는 것으로 기존 대부분의 방범시스템이 사용하는 RS-485 통신 프로토콜을 재설계하여 화상감시 시스템과의 통신을 설계하였으며 둘째는 화상감시시스템과 관제센터 시스템간의 통신을 설계하는 것으로 현재 화상감시 시스템의 TCP/IP 프로토콜을 이용한 socket 통신으로 관제센터 시스템과의 실시간 데이터 전송을 가능하게 했다. 이 시스템을 활용할 경우 고객들은 반드시 관제센터시스템의 인증을 거쳐야 하므로 고객의 DataBase를 축적할수 있으며 이 정보를 활용하여 인터넷 화상방범 서비스 Potal Site구축이 가능하다는 장점이 있다.

• Physical Therapy Korea
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• v.15 no.2
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• pp.38-43
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• 2008

To reduce winging scapula, various exercise protocols have been widely used by clinicians. Selective serratus anterior strengthening, and restoring balanced function, are especially recommended to reduce winging scapula. The purpose of this study was to investigate visual biofeedback using a real time video camera display system for monitoring scapular winging during arm lowering. For this study, 13 males with winging scapular were recruited during arm lowering. Electromyography (EMG) activity was recorded from the serratus anterior (SA) and upper trapezius (UT) of the right side and compared with normal EMG activity using a paired t-test. The study showed, through visual biofeedback, that EMG activity significantly increased in the SA and significantly decreased in the UT (p<.05). These results suggest that visual biofeedback can be recommended as an effective method for scapular eccentric control, to prevent scapular winging during arm lowering.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.7-15
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• 2010

Recently, there are many home network applications using the convergence between mobile platforms and home network system. As smart phones which are called small PC in the hand becoming popular, the development of home network application beyond the one of cellular phones is required. Android mobile platform of Google includes OS, middleware and primary applications. Also, in addition to smart phones, it can be mounted on various devices such as set-top box and household appliances. In this paper, home network services on smart phones of Android platform are designed and implemented. The services provide real-time monitoring information (temperature, humidity, real-time video) of rooms and appliance control in a house.

• Journal of the Korean Society of Safety
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• v.29 no.2
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• pp.12-17
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• 2014

This paper is aimed to analyze the characteristics of simultaneous voltage, current and temperature signals for poor connection on electrical connector. In order to attain this purpose, detected were the current and voltage signals on electric wire with series arc, named arc signals, and also monitored were the changes of RMS, instantaneous value of waveform in time domain and temperature value with video. Two states are made normal state over $5kgf{\cdot}cm$ and poor connections state below $0.5kgf{\cdot}cm$ by screw gage. In the voltage signal case, the voltage drop was increased with which the current was increased. In the current signal case, poor connections at the time interval 1~4A all showed "shoulder", as distinct difference from the normal state shown waveform pattern. In the temperature signal case, poor connections are twice at 1A and five times at 4A in the normal state. The temperature continues insulation of electrical wiring and connector can be carbonized. The results of this study will be effectively used in developing the preventive devices and system for electric fire by poor connections.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.136-145
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• 2019

The crowded environment of a domestic pig farm is highly vulnerable to the spread of infectious diseases such as foot-and-mouth disease, and studies have been conducted to automatically analyze behavior of pigs in a crowded pig farm through a video surveillance system using a camera. Although it is required to correctly separate occluding pigs for tracking each individual pigs, extracting the boundaries of the occluding pigs fast and accurately is a challenging issue due to the complicated occlusion patterns such as X shape and T shape. In this study, we propose a fast and accurate method to separate occluding pigs not only by exploiting the characteristics (i.e., one of the fast deep learning-based object detectors) of You Only Look Once, YOLO, but also by overcoming the limitation (i.e., the bounding box-based object detector) of YOLO with the test-time data augmentation of rotation. Experimental results with two-pigs occlusion patterns show that the proposed method can provide better accuracy and processing speed than one of the state-of-the-art widely used deep learning-based segmentation techniques such as Mask R-CNN (i.e., the performance improvement over Mask R-CNN was about 11 times, in terms of the accuracy/processing speed performance metrics).

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.213-220
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• 2023

Face recognition is a very important process in video monitoring and is a type of biometric technology. It is mainly used for identification and security purposes, such as ID cards, licenses, and passports. The recognition process has many variables and is complex, so development has been slow. In this paper, we proposed a face recognition method using CNN, which has been re-examined due to the recent development of computers and algorithms, and compared with the feature comparison method, which is an existing face recognition algorithm, to verify performance. The proposed face search method is divided into a face region extraction step and a learning step. For learning, face images were standardized to 50×50 pixels, and learning was conducted while minimizing unnecessary nodes. In this paper, convolution and polling-based techniques, which are one of the deep learning technologies, were used for learning, and 1,000 face images were randomly selected from among 7,000 images of Caltech, and as a result of inspection, the final recognition rate was 98%.

• Journal of Korean Neurosurgical Society
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• v.44 no.1
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• pp.26-35
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• 2008

Objective : In the evaluation of patients with Parkinson's disease (PD), most neurologists only see their patients during a limited period of their fluctuating 24-hour-a-day lives. This study aimed to assess the short-term outcome of STN stimulation for patients with advanced PD evaluated in a 24-hour monitoring unit for movement disorder (MUMD) using a prospective protocol. Methods : Forty-two patients with advanced PD consecutively treated with bilateral STN stimulation using multi-channel microelectrode recording were included in this study. All patients were evaluated using a 24-hour MUMD with a video recording/editing system and were evaluated with a prospective protocol of the Unified Parkinson's Disease Rating Scale, Hoehn and Yahr Staging, Schwab and England Activities of Daily Living, levodopa equivalent daily dose (LEDD), Short Form-36 Health Survey, and neuropsychological tests. Magnetic resonance (MR) images of the brain were performed prior to and six months after surgery. Results : All patients were evaluated at three and six months after surgery. There was a rapid and significant improvement of the motor symptoms, especially in tremor and rigidity, after STN stimulation with low morbidity. Dyskinesia was markedly decreased with much lowered LEDD values by 50% after STN stimulation. 1.5T MR images were safely taken according to the manufacturer's guidelines at six months after surgery without any adverse effects in 41 patients treated with STN stimulations. Conclusion : Evaluations in a 24-hour monitoring unit could reduce the dose of medication efficiently to an optimal level with patients' comfort and improve the clinical symptoms in harmony with STN stimulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8C
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• pp.711-720
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• 2012

In this paper, we introduce the implementation of driver fatigue monitering system and its result. Input video device is selected commercially available web-cam camera. Haar transform is used to face detection and adopted illumination normalization is used for arbitrary illumination conditions. Facial image through illumination normalization is extracted using Haar face features easily. Eye candidate area through illumination normalization can be reduced by anthropometric measurement and eye detection is performed by PCA and Circle Mask mixture model. This methods achieve robust eye detection on arbitrary illumination changing conditions. Drowsiness state is determined by the level on illumination normalize eye images by a simple calculation. Our system alarms and operates seatbelt on vibration through controller area network(CAN) when the driver's doze level is detected. Our algorithm is implemented with low computation complexity and high recognition rate. We achieve 97% of correct detection rate through in-car environment experiments.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.209-214
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• 2018

The subway, which is an urban railway, is the core of public transportation. Urban railways are always exposed to serious problems such as theft, crime and terrorism, as many passengers use them. Especially, due to the nature of urban railway environment, the scope of surveillance is widely dispersed and the range of surveillance target is rapidly increasing. Therefore, it is difficult to perform comprehensive management by passive surveillance like existing CCTV. In this paper, we propose the implementation, design method and object recognition algorithm for intelligent object recognition system in urban railway. The object recognition system that we propose is to analyze the camera images in the history and to recognize the situations where there are objects in the landing area and the waiting area that are not moving for more than a certain time. The proposed algorithm proved its effectiveness by showing detection rate of 100% for Selected area detection, 82% for detection in neglected object, and 94% for motionless object detection, compared with 84.62% object recognition rate using existing Kalman filter.