• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2020년도 추계학술발표대회
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• pp.967-970
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• 2020

In a previous work we have done, we presented a monitoring system to automatically detect some dogs' behaviors from videos. However, the input video data used by that system was pre-trimmed to ensure it contained a dog only. In a real-life situation, the monitoring system would continuously receive video data, including frames that are empty and ones that contain people. In this paper, we propose a YOLOv4-based system for automatic object detection and trimming of dog videos. Sequences of frames trimmed from the video data received from the camera are analyzed to detect dogs and people frame by frame using a YOLOv4 model, and then records of the occurrences of dogs and people are generated. The records of each sequence are then analyzed through a rule-based decision tree to classify the sequence, forward it if it contains a dog only or ignore it otherwise. The results of the experiments on long untrimmed videos show that our proposed method manages an excellent detection performance reaching 0.97 in average of precision, recall and f-1 score at a detection rate of approximately 30 fps, guaranteeing with that real-time processing.

• 한국정보전자통신기술학회논문지
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• 제11권1호
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• pp.82-88
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• 2018

기존의 원격 화재 모니터링 시스템은 화재 현장의 영상 정보를 실시간으로 확인 할 수 있는 기능이 충분하지 않아 화재 발생 시 실제 상황을 면밀히 파악하여 대처하는데 어려움이 있었다. 이러한 문제를 해결하기 위하여 본 논문에서는 RTSP 모듈을 이용한 원격 화재 영상 모니터링 시스템 설계 방안을 제안한다. 화재 현장을 실시간으로 문자와 경보음 및 영상 정보를 확인하기 위한 장치로서 RTSP 모듈, 전용서버와 클라이언트 그리고 원격 화재 모니터링 시스템으로 구성한다. 이동형로봇 등에 부착된 카메라에서 송수신하는 영상을 Wi­Fi를 이용하여 RTSP 기능이 포함된 서버 및 클라이언트 시스템과 연동한다. 설계한 실시간 동영상의 수신이 가능한 일체형 원격 화재 영상 모니터링 시스템을 구현하고 시험을 통하여 정상적으로 동영상이 수신됨을 확인 하였다.

• 한국통신학회논문지
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• 제31권4A호
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• pp.421-430
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• 2006

본 논문에서는 IEEE 802.11a 무선랜(WLAN) 환경에서 Cross Layer 기반의 채널 모니터링(Cross-Layered Monitoring: CLM)을 이용한 네트워크 적응형 고선명(high definition: HD) MPEG-2 TS 비디오 스트리밍 시스템을 제안한다. 무선 채널 모니터링을 위해 AE(access point)는 MAC(medium access control) 계층의 전송 상태를 주기적으로 측정하고 응용 계층의 스트리밍 서버로 전달한다. 이것은 비디오 스트리밍 응용 프로그램이 피드백 기반의 종단간 모니터링(End-to-End Monitoring: E2EM) 기법을 적용할 때보다 가변적인 무선 채널 상태에 좀 더 빠르고 효과적으로 적응할 수 있게 한다. 스트리밍 서버는 네트워크에 적응적인 전송을 위해 측정된 무선 채널 상태에 따라 우선순위 기반의 프레임 폐기(priority-based frame dropping)를 수행한다. 이를 위해 스트리밍 서버는 실시간 파싱(real-time parsing)과 프레임 기반의 패킷 우선순위화(frame-based prioritized packetization) 기능을 제공한다. 성능 평가를 위해 IEEE 802.11a 무선랜 환경에서의 다양한 스트리밍 실험을 수행한다. 실험 결과는 제안 시스템이 시간에 따라 가변하는 무선 채널 상태에서 기존 기법에 비해 종단간 비디오 스트리밍의 품질을 향상시킬 수 있음을 보여준다.

• Journal of Ubiquitous Convergence Technology
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• 제1권1호
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• pp.3-8
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• 2007

This paper presents a set of techniques used in a real-time visual surveillance system. The system is implemented on a low-cost embedded DSP platform that is designed to work with stationary video sources. It consists of detection, a tracking and a classification module. The detector uses a statistical method to establish the background model and extract the foreground pixels. These pixels are grouped into blobs which are classified into single person, people in a group and other objects by the dynamic periodicity analysis. The tracking module uses mean shift algorithm to locate the target position. The system aims to control the human density in the surveilled scene and detect what happens abnormally. The major advantage of this system is the real-time capability and it only requires a video stream without other additional sensors. We evaluate the system in the real application, for example monitoring the subway entrance and the building hall, and the results prove the system's superior performance.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2009년도 춘계학술발표대회
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• pp.35-38
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• 2009

The paper presents an effective method to detect fire in video surveillance and monitoring system. The main contribution of this work is that we successfully use the Hidden Markov Models in the process of detecting the fire with a few preprocessing steps. First, the moving pixels detected from image difference, the color values obtained from the fire flames, and their pixels clustering are applied to obtain the image regions labeled as fire candidates; secondly, utilizing massive training data, including fire videos and non-fire videos, creates the Hidden Markov Models of fire and non-fire, which are used to make the final decision that whether the frame of the real-time video has fire or not in both temporal and spatial analysis. Experimental results demonstrate that it is not only robust but also has a very low false alarm rate, furthermore, on the ground that the HMM training which takes up the most time of our whole procedure is off-line calculated, the real-time detection and alarm can be well implemented when compared with the other existing methods.

• Spatial Information Research
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• 제16권4호
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• pp.411-420
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• 2008

국토모니터링기술개발 핵심과제는 지능형국토정보기술혁신사업단의 5개 핵심과제 중 하나로서 토지피복변화가 빈번한 한반도 전역의 국토변화를 주기적/실시간으로 모니터링하기 위한 기술적 기반을 제공하고자 한다. 이 과제는 크게 두개의 연구주제를 포함하고 있는데, 첫번째 주제는 공중 및 지상에서 실시간으로 국토모니터링을 위한 자료 획득을 다루고 있다. 디지털항공사진 및 항공 LiDAR 자료를 실시간으로 획득하기 위한 영상시스템과 USN, 지상 비디오 영상, 차량탑재 센서를 통한 지상자료획득 시스템을 개발하여 공중원격탐사자료의 한계를 극복하기 위한 자료획득시스템을 개발하고자 한다. 두 번째 주제는 국토모니터링을 담당하고 있는 공공기관에서 직접 채택 운영될 수 있는 여러 활용시스템을 개발하고 그에 필요한 제반 처리기술을 개발하고자 한다. MODIS 위성자료를 이용한 국토모니터링 시스템은 그러한 활용시스템의 하나로서 준 실시간으로 한반도 전역의 토지피복 상황을 모니터링하기 위한 기술을 포함하고 있다.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.15-18
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• 2004

In this paper, it is implemented the monitoring system for TV advertisement using video watermark. The functions of an advertisement monitoring system are automatically monitoring for the time, length, and index of the on-air advertisement, saving the log data, and reporting the monitoring result. The performance of the video watermark used in this paper is tested for TV advertisement monitoring. This test includes LAB test and field test. LAB test is done in laboratory environment and field test in actually broadcasting environment. LAB test includes PSNR, distortion measure in image, and the watermark detection rate in the various attack environment such as AD/DA(analog to digital and digital to analog) conversion, noise addition, and MPEG compression The result of LAB test is good for the TV advertisement monitoring. KOBACO and SBS are participated in the field test. The watermark detection rate is 100% in both the real-time processing and the saved file processing. The average deviation of the watermark detection time is 0.2 second, which is good because the permissible average error is 0.5 second.

• 한국전자통신학회논문지
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• 제7권1호
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• pp.21-26
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• 2012

본 논문에서는 HSDPA 상용 무선 네트워크 환경을 이용하여 실시간으로 영상 데이터를 전송하고 위치를 인식하는 시스템을 제안한다. 이번 연구에서는 MPEG4를 기반으로 하는 새로운 영상 압축 알고리듬을 개발하여 130 kbps 대역폭과 30 fps의 QVGA 영상 전송률을 실현하였다. 이동 차량에 탑재할 목적으로 본 시스템을 소형화하고 전력 효율을 좋게 하였으며 외란에도 견실하게 설계하였다. 시스템을 실제 구동시켜 얻은 동영상 캡쳐 화면과 위치 인식 데이터를 제시하여 개발한 시스템의 성능을 검증한다. 본 시스템은 순찰차 및 대중교통 시스템에 적용하는 것을 목표로 하고 있으며 유선 전송이 어려운 오지 환경에서 실시간으로 영상정보를 획득하고자 할 때도 적용 가능하다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1189-1204
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• 2018

Detecting and capturing 3D human structures from the intensity-based image sequences is an inherently arguable problem, which attracted attention of several researchers especially in real-time activity recognition (Real-AR). These Real-AR systems have been significantly enhanced by using depth intensity sensors that gives maximum information, in spite of the fact that conventional Real-AR systems are using RGB video sensors. This study proposed a depth-based routine-logging Real-AR system to identify the daily human activity routines and to make these surroundings an intelligent living space. Our real-time routine-logging Real-AR system is categorized into two categories. The data collection with the use of a depth camera, feature extraction based on joint information and training/recognition of each activity. In-addition, the recognition mechanism locates, and pinpoints the learned activities and induces routine-logs. The evaluation applied on the depth datasets (self-annotated and MSRAction3D datasets) demonstrated that proposed system can achieve better recognition rates and robust as compare to state-of-the-art methods. Our Real-AR should be feasibly accessible and permanently used in behavior monitoring applications, humanoid-robot systems and e-medical therapy systems.