• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.42-45
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• 2016

본 논문은 기동성은 있지만 한정된 비행시간과 비행거리를 가진 드론을 이용한 지능형 영상 보안 감시 시스템을 제안한다. 드론이 가지는 한계점을 보완하기 위해 그리드 기반으로 시스템을 구성하며 분할된 영역에서 다중 드론간 대상물의 효율적인 추적 및 감시 모니터링을 위해 연계 추적 방식을 이용한다. 먼저, 한정된 비행거리를 위해 각 드론스테이션 간의 최적 거리를 제안한다. 제안한 최적 거리를 통해 생성된 중첩 감시영역에서 효율적 연계 추적을 위해 드론의 전력상태와 대상물의 이동방향을 고려한 최적의 드론 선정 알고리즘을 제시한다. 제안한 알고리즘은 케이스 스터디를 통해 그 응용 가능성을 검토한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.786-791
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• 2007

In the place that its security is crucial, the necessity of system which can tract and recognize random person is getting more important. In this paper, we'd like to develop the invader tracking system which consists of the sound source tracking-sensor and the pan-tilt camera for wide-area guard. After detecting the direction of any sound with the sound source tracking-sensor at first, our system make move the pan-tilt camera to that direction and extract reference image from that camera. This reference image is compared and updated by the next captured image after some interval time. By keeping on it over again, we can realize the guard system which can tract an invader using the difference image and the result of another image processing. By linking home network security system, the suggested system can provide some interfacing functions for the security service of the public facilities as well as that of home.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.488-491
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• 2012

최근 카메라 센서 및 알고리즘의 발달로 엔터테인먼트 목적의 영상 시스템을 비롯한 공정 기술, 교육 및 의료 등 다양한 목적의 영상 시스템이 개발 되고 있다. 또한 범죄 예방, 사고 상황 인식을 위한 감시 영상 시스템의 연구도 활발히 진행되고 있다. 본 논문에서는 이상 상황 인식을 위한 지능형 교통 시스템에 대해 제안하고자 한다. 제안하는 시스템은 크게 학습 과정과 이상 상황 인식 과정으로 나누어진다. 학습 과정에서는 CCTV와 같은 정적인 카메라에서 촬영된 도로 교통 영상에서 이동 객체의 특징을 추출하고 이를 추적하여 특징 벡터를 구성한다. 구성된 특징 벡터들은 클러스터링 기법을 통해 장면을 모델링하는데 이용되며 최종적으로 이 모델을 이용해 실시간으로 도로 교통 영상에서 이상 상황을 인식할 수 있게 된다. 실험을 통한 성능 평가를 통해 시스템의 우수함을 확인 하였다.

• Electronics and Telecommunications Trends
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• v.26 no.6
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• pp.139-153
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• 2011

1990년 후반 이후, 스마트 카메라가 널리 대중화되면서 비디오 감시(video surveillance) 분야와 머신 비전(machine vision) 분야의 산업에서 스마트 카메라가 사용되기 시작했다. 스마트 카메라는 단순히 영상정보를 획득하고 획득한 영상정보를 저장하는 기존의 카메라 기능에서 벗어나, 미리 정해진 여러 가지 필요한 작업을 수행하는 비전시스템으로 정의할 수 있다. 특히, 최근 들어 마이크로프로세서의 기능이 확대되면서 카메라 내부에서 지능형 영상처리나 패턴인식 알고리즘을 수행할 수 있게 되었으며, 이러한 기술을 이용해서 스마트 카메라는 움직임 감지, 오브젝트 측정, 차량의 번호판 인식뿐만 아니라 인간의 행동까지도 인식할 수 있게 되었다. 오늘날 스마트 카메라는 빌딩관리나 빌딩제어 분야 애플리케이션의 핵심 디바이스가 되었으며, 향후에는 우리 주변 곳곳에 스며들어 주변 환경에 따라 지능적으로 대처할 수 있는 유비쿼터스 환경의 핵심 기술로 자리매김하게 될 것이다. 본 고에서는 스마트 카메라의 기술적인 정의와 특징을 살펴본 후에 스마트 카메라의 기술적인 동향들을 살펴볼 것이다.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.9
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• pp.22-30
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• 2011

Recently, the rising demand for intelligent video surveillance system leads to high-performance face recognition systems. The solution for low-resolution images acquired by a long-distance camera is required to overcome the distance limits of the existing face recognition systems. For that reason, this paper proposes a hardware design of an image resolution enhancement algorithm for real-time intelligent video surveillance systems. The algorithm is synthesizing a high-resolution face image from an input low-resolution image, with the help of a large collection of other high-resolution face images, called training set. When we checked the performance of the algorithm at 32bit RISC micro-processor, the entire operation took about 25 sec, which is inappropriate for real-time target applications. Based on the result, we implemented the hardware module and verified it using Xilinx Virtex-4 and ARM9-based embedded processor(S3C2440A). The designed hardware can complete the whole operation within 33 msec, so it can deal with 30 frames per second. We expect that the proposed hardware could be one of the solutions not only for real-time processing at the embedded environment, but also for an easy integration with existing face recognition system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.357-358
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• 2021

Although many people watch the scene with multiple surveillance cameras, it is difficult to ensure that immediate action can be taken in the event of a crime. Therefore, there is a need for a "crime behavior detection system" that can analyze images in real time from multiple surveillance cameras installed in elevators, call immediate crime alerts, and track crime scenes and times effectively. In this paper, a study was conducted to detect violent scenes occurring in elevators using Scene Change Detection. For effective detection, an x2-color histogram combining color histogram and histogram was applied.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.237-239
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• 2021

Failure and malfunction of the unmanned surveillance facility cost can lead to delays occurring until the person in charge arrives at the unmanned surveillance facility, and theft, damage, and information leakage damage caused by intruders. In addition, due to equipment failure and malfunction, additional costs are incurred due to constant inspection by the manager. In this paper, in order to compensate for the malfunction of unmanned facility costs, we propose a system that diagnoses the monitoring facility in real time, displays the contents of the problem, automatically restores the facility power, and informs the person in charge of the situation by text message. The proposed system is a surveillance facility consisting of main facilities such as video equipment (CCTV), sound equipment, floodlights, etc. And SMS server that can send text messages in real time. Through experiments, the effectiveness of the proposed system was verified.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.11-22
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• 2010

This paper is a study on implementation of intelligent image surveillance system using context information and supplements temporal-spatial constraint, the weak point in which it is hard to process it in real time. In this paper, we propose scene analysis algorithm which can be processed in real time in various environments at low resolution video(320*240) comprised of 30 frames per second. The proposed algorithm gets rid of background and meaningless frame among continuous frames. And, this paper uses wavelet transform and edge histogram to detect shot boundary. Next, representative key-frame in shot boundary is selected by key-frame selection parameter and edge histogram, mathematical morphology are used to detect only motion region. We define each four basic contexts in accordance with angles of feature points by applying vertical and horizontal ratio for the motion region of detected object. These are standing, laying, seating and walking. Finally, we carry out scene analysis by defining simple context model composed with general context and emergency context through estimating each context's connection status and configure a system in order to check real time processing possibility. The proposed system shows the performance of 92.5% in terms of recognition rate for a video of low resolution and processing speed is 0.74 second in average per frame, so that we can check real time processing is possible.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.2
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• pp.344-352
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• 2003

An effective algorithm for implementation of which detects moving object from image sequences. predicts the direction of it. and drives the camera in real time is proposed. In static camera, for robust motion detection from a dynamic background scene, the proposed algorithm performs statistical modeling of moving objects and background, and trains the statistical modeling of moving objects and background, and trains the statistical feature of background with the initial parts of sequence which have no moving objects. Active camera moving objects are segmented by following procedure, an improved order adaptive lattice structured linear predictor is used. The proposed algorithm shows robust object tracking results in the environment of static or active camera. It can be used for the unmanned surveillance system, traffic monitoring system, and autonomous vehicle.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.165-171
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• 2018

Shadow is a physical phenomenon observed in natural scenes and has a negative effect on various image processing systems such as intelligent video surveillance, traffic surveillance and aerial imagery analysis. Therefore, shadow detection should be considered as a preprocessing process in all areas of computer vision. In this paper, we define and analyze various feature elements for shadow detection in a single natural image that does not require a reference image. The shadow elements describe the intensity, chromaticity, illuminant-invariant, color invariance, and entropy image, which indicate the uncertainty of the information. The results show that the chromaticity and illuminant-invariant images are effective for shadow detection. In the future, we will define a fusion map of various shadow feature elements, and continue to study shadow detection that can adapt to various lighting levels, and shadow removal using chromaticity and illuminance invariant images.