• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.57-64
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• 2020

Video surveillance is consists of cameras, transmission devices, storage and playback devices, and is used for crime prevention and disaster monitoring. Recently, it has been spreading to a wide variety of fields, and has developed into an intelligent video surveillance system that can automatically recognize or track characteristic objects of people and things. The purpose of this study was to investigate the cases of video surveillance service applying the latest technology by dividing it into the home, public, and private sectors. also this study tried to investigate and research what advantage it brings from a business perspective. By looking at the cases introduced in this study, it was confirmed that the viedo security service is developing intelligently, such as excellent compatibility with CCTV, multiple video surveillance, CCTV screen motion detection, and alarm through automatic analysis.

• Journal of Digital Contents Society
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• v.17 no.4
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• pp.273-278
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• 2016

Disasters are linked with some attributes of modern society in diversity, complexity and unpredictability, so various approach and remedies of them will appease the nation's anxiety upon them. For this reason, in this paper we review the building plans and its application and its application Province pros and cons of intelligent video surveillance analysis system for social security, the future proposed service is the safety of people while performing a comprehensive urban traffic control functions as a video monitoring system for social security and to ensure, to prevent crime and accidents, it is to crack down on criminal offenses in advance expected to protect the property of citizens and public facilities.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.418-424
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• 2014

In this paper, we propose a boundary input method for video surveillance systems. Since intrusion of a moving object is decided by comparition of its position and the surveillance boundary, the boundary input method is a basic function in video surveillance. Previous methods are difficult to adapt to the change of surveillance environments such as the size of surveillance area, the number of cameras, and the position of cameras because those build up the surveillance boundary using the captured image in the center of each camera. In our approach, the whole surveillance boundary is once defined in the form of polygon based on the satellite map and transformed into each camera environment. Its characteristics is that the boundary input is independent from the surveillance environment. Given the position of a moving object, the time complexity of its intrusion detection shows O(n), where n is the number of polygon vertices. To verify our method, we implemented a 3D simulation and assured that the input boundary can be reused in each camera without any redefinition.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.3
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• pp.63-69
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• 2012

In this paper, we introduced design of intelligent surveillance camera system and typical event processing scenario for urban transit. To analyze video, we studied events that frequently occur in surveillance camera system. Event processing scenario is designed for seven representative situations(designated area intrusion, object abandon, object removal in designated area, object tracking, loitering and congestion measurement) in urban transit. Our system is optimized for low hardware complexity, real time processing and scenario dependent solution.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.4
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• pp.164-168
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• 2013

Recently, in the elevator of the buildings, such as apartment and office building, the visitor monitoring cameras for surveillance, advertising LCD to provide a variety of information, emergency call devices, and safe driving information sensors are installed. A variety of multimedia data from these devices to the central control office, management office, or to guard room are transmitted in real-time. Each sub-systems in the elevator are installed with a separate lines and operated independently and use different management principals, so integrated management for each sub-systems are very difficult. In this study, we develop a new IP-based multi-channel video surveillance system which is integrated surveillance camera, emergency call devices, communications devices, various sensors in the elevator, DVR, ODM, and can manage all devices by two-way communication and integrated interface. And we evaluated the performance of the developed system.

• The Journal of Korean Institute of Information Technology
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• v.17 no.10
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• pp.101-113
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• 2019

Recently, the intelligent video surveillance technology has become able to provide various services such as predictive surveillance that have not been provided previously. Securing the security of the intelligent video surveillance is essential, and malicious manipulation of the original CCTV video data can lead to serious social problems. Therefore, in this paper, we proposed an intelligent video surveillance environment based on blockchain. The proposed scheme guarantees the integrity of the CCTV image data and protects the ROI privacy through the edge blockchain, so there is no privacy exposure of the object. In addition, it is effective because it is possible to increase the transmission efficiency and reduce storage by enabling video deduplication.

• Journal of Electrical Engineering and Technology
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• v.7 no.1
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• pp.115-123
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• 2012

A video analysis system used to detect events in video streams generally has several processes, including object detection, object trajectories analysis, and recognition of the trajectories by comparison with an a priori trained model. However, these processes do not work well in a complex environment that has many occlusions, mirror effects, and/or shadow effects. We propose a new approach to a context-aware video surveillance system to detect predefined contexts in video streams. The proposed system consists of two modules: a feature extractor and a context recognizer. The feature extractor calculates the moving energy that represents the amount of moving objects in a video stream and the stationary energy that represents the amount of still objects in a video stream. We represent situations and events as motion changes and stationary energy in video streams. The context recognizer determines whether predefined contexts are included in video streams using the extracted moving and stationary energies from a feature extractor. To train each context model and recognize predefined contexts in video streams, we propose and use a new ensemble classifier based on the AdaBoost algorithm, DAdaBoost, which is one of the most famous ensemble classifier algorithms. Our proposed approach is expected to be a robust method in more complex environments that have a mirror effect and/or a shadow effect.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.176-178
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• 2005

In the images of a video camera employed for surveillance, detecting targets by extracting foreground image is of great importance. The foreground regions detected, however, include not only moving targets but also their shadows. This paper presents a novel technique to detect shadow pixels in the foreground image of a video camera. The image characteristics of video cameras employed, a web-cam and a CCD, are first analysed in the HSV color space and a pixel-level shadow detection technique is proposed based on the analysis. Compared with existing techniques where unified criteria are used to all pixels, the proposed technique determines shadow pixels utilizing a fact that the effect of shadowing to each pixel is different depending on its brightness in background image. Such an approach can accommodate local features in an image and hold consistent performance even in changing environment. In experiments targeting pedestrians, the proposed technique showed better results compared with an existing technique.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.1
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• pp.28-35
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• 2014

Surveillance cameras in national border and coastline area often occur the video distortion because of rapidly changing weather and light environments. It is positively necessary to enhance the distorted video quality for keeping surveillance. In this paper, we propose an adaptive video enhancement algorithm in the various environment changes. To solve an unstable performance problem of the existing method, the proposed method is based on Retinex algorithm and uses enhanced curves which is adapted in foggy and low-light conditions. In addition, we mixture the weighted HSV color model to keep color constancy and reduce noise to obtain clear images. As a results, the proposed algorithm improves the performance of well-balanced contrast enhancement and effective color restoration without any quality loss compared with the existing algorithm. We expect that this method will be used in surveillance camera systems and offer help of national defence with reliability.

• 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.