• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1262-1270
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• 2020

Recently, the smart video surveillance research, which has been receiving increasing attention, has mainly focused on the intruder detection and tracking, and abandoned object detection. On the other hand, research on real-time detection of stolen objects is relatively insufficient compared to its importance. Considering various smart surveillance video application environments, this paper presents two different types of stolen object detection algorithms. We first propose an algorithm that detects theft of statically and dynamically registered surveillance objects using a dual background subtraction model. In addition, we propose another algorithm that detects theft of general surveillance objects by applying the dual background subtraction model and Mask R-CNN-based object segmentation technology. The former algorithm can provide economical theft detection service for pre-registered surveillance objects in low computational power environments, and the latter algorithm can be applied to the theft detection of a wider range of general surveillance objects in environments capable of providing sufficient computational power.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1009-1015
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• 2017

Recently, video surveillance is becoming more and more common as many camera are installed due to crime, terrorism, traffic and security. And systems that control cameras are becoming increasingly general. Video input from the installed camera is monitored by the multiscreen at the central control center, it is essential to simultaneously monitor multiscreen in real-time to quickly respond to situations or dangers. However, monitoring of multiscreen in a mobile environment such as a smart phone is not applied to hardware specifications or network bandwidth problems. For resolving these problems, in this paper, we propose a system that can monitor multiscreen in real-time in mobile-phone environment. We reconstruct the desired multiscreen through transcoding, it is possible to monitor continuously video streaming of multiple cameras, and to have the advantage of being mobile in mobile-phone environment.

• Smart Media Journal
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• v.13 no.4
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• pp.65-75
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• 2024

This paper proposes a new approach to video surveillance systems, which have become essential components in modern urban management. By utilizing blockchain and IPFS, it enhances data integrity and privacy protection. Additionally, anomaly detection and automatic video storage are enabled through object detection technology, thus improving urban safety and security. This integrated approach serves as an efficient management methodology for surveillance systems, providing city administrators and citizens with a safer and more effective monitoring environment.

• 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.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.6
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• pp.7-14
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• 2019

The automatic monitoring and detection of crowd behavior in the surveillance videos has obtained significant attention in the field of computer vision due to its vast applications such as security, safety and protection of assets etc. Also, the field of crowd analysis is growing upwards in the research community. For this purpose, it is very necessary to detect and analyze the crowd behavior. In this paper, we proposed a deep learning-based method which detects abnormal activities in surveillance cameras installed in a smart city. A fine-tuned VGG-16 model is trained on publicly available benchmark crowd dataset and is tested on real-time streaming. The CCTV camera captures the video stream, when abnormal activity is detected, an alert is generated and is sent to the nearest police station to take immediate action before further loss. We experimentally have proven that the proposed method outperforms over the existing state-of-the-art techniques.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.9
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• pp.953-958
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• 2015

Raspberry Pi is a credit card-sized computer with support for a large number of input and output peripherals. This makes it the perfect platform for interaction with many different devices and for usage in a wide range of applications. When combined with Wi-Fi, it can communicate remotely, therefore increasing its suitability for the construction of wireless sensor nodes. In addition, data processing and decision-making can be based on artificial intelligence, what is performed in developed testbed on the example of monitoring and determining the confidence of fire. In this paper, we demonstrated the usage of Raspberry Pi as a sensor web node for fire-safety monitoring in a building. When the UV-flame sensors detect a flame as thin as that of a candle, the Raspberry Pi sends a push-message to notify the assigned smartphone of the on-site situation through the GCM server. A mobile app was developed to provide a real-time video streaming service in order to determine a false alarm. If an emergency occurs, one can immediately call for help.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.17-22
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• 2009

DaVinci processors are popular media processors for implementing embedded multimedia applications. They support dual core architecture: ARM9 core for video I/O handling as well as system management and peripheral handling, and DSP C64+ core for effective digital signal processing. In this paper, we propose our efforts for optimal implementation of object tracking algorithm in DaVinci-based smart camera which is being designed and implemented by our laboratory. The smart camera in this paper is supposed to support object detection, object tracking, object classification and detection of intrusion into surveillance regions and sending the detection event to remote clients using IP protocol. Object tracking algorithm is computationally expensive since it needs to process several procedures such as foreground mask extraction, foreground mask correction, connected component labeling, blob region calculation, object prediction, and etc. which require large amount of computation times. Thus, if it is not implemented optimally in Davinci-based processors, one cannot expect real-time performance of the smart camera.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.4
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• pp.461-466
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• 2016

In this paper, a capsule body shaped surveillance/monitoring device is developed. The device includes a camera and GPS module to transmit live video data and real time GPS coordinates respectively using the Intel Edison module. A control application is developed for the smart phones and tablets to wirelessly view the live video stream and location of the capsule device and also to switch between the multiple capsule devices installed at different locations. The coordination between the developed device and the smart phone / tablet is done using the wireless function of the Intel Edison module.

• Smart Media Journal
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• v.1 no.2
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• pp.12-22
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• 2012

Today we are witnessing an increasingly widespread use of cameras in our lives for video surveillance, robot vision, and mobile phones. This has led to a renewed interest in computer vision in general and an on-going boom in human activity recognition in particular. Although not particularly fancy per se, human gait is inarguably the most common and frequent action. Early on this decade there has been a passing interest in human gait recognition, but it soon declined before we came up with a systematic analysis and understanding of walking motion. This paper presents a set of DBN-based models for the analysis of human gait in sequence of increasing complexity and modeling power. The discussion centers around HMM-based statistical methods capable of modeling the variability and incompleteness of input video signals. Finally a novel idea of extending the discrete state Markov chain with a continuous density function is proposed in order to better characterize the gait direction. The proposed modeling framework allows us to recognize pedestrian up to 91.67% and to elegantly decode out two independent gait components of direction and posture through a sequence of experiments.