• Smart Media Journal
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• v.7 no.2
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• pp.40-46
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• 2018

The conventional CCTV surveillance system for preventing accidents and incidents misses 95% of the data after 22 minutes where one person monitors multiple CCTV. To address this issue, researchers have studied the computer-based intelligent video surveillance system for notifying people of the abnormal situation. However, because the system is involved in the problems of power consumption and costs, the intelligent video surveillance system based on embedded modules has been studied. This paper implements the intelligent video surveillance system based on embedded modules for detecting intruders, detecting fires and detecting loitering, falling. Moreover, the algorithm and the embedded module optimization method are applied to implement real-time processing. The intelligent video surveillance system based on embedded modules is implemented in Raspberry Pi. The algorithm processing time is 0.95 seconds on Raspberry Pi before optimization, and 0.47 seconds on Raspberry Pi after optimization, reduced processing time by 50.52%. Therefore, this suggests real processing possibility of the intelligent video surveillance system based on the embedded modules is possible.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.556-557
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• 2019

The current video surveillance system is the third generation, and the video device has developed from low image quality to high image quality. The video surveillance solution has improved from the simple type to the intelligent type. However, as the equipment and technology for these video surveillance systems become more complicated and diversified, they are increasingly dependent on infrastructure, such as faster network speed and stable power supply. On the other hand, there is a growing need for video surveillance in areas where basic infrastructure is limited, such as power and communications. In this paper, we propose a system that can support intelligent video surveillance in a region where basic infrastructure is limited.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.213-216
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• 2022

There has been an increase in video surveillance for public safety and security, which increases the video data, leading to analysis, and storage issues. Furthermore, most surveillance videos contain an empty frame of hours of video footage; thus, extracting useful information is crucial. The prominent framework used in surveillance for efficient storage and analysis is video synopsis. However, the existing video synopsis procedure is not applicable for creating an abnormal object-based synopsis. Therefore, we proposed a lightweight synopsis methodology that initially detects and extracts abnormal foreground objects and their respective backgrounds, which is stitched to construct a synopsis.

• 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.

• 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.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.1360-1365
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• 2008

Most recognition algorithms for intelligent surveillance system are based on analysis of the video collected from one camera. Video analysis is also used to compute the internal parameters used in the recognition process. The algorithm computes only the video of the fixed area so that it is a insufficient method and it could not use information of the related areas. However, intelligent integrated surveillance system should be constructed to correlate the events in the other areas as well as in the fixed area. In this paper, in order to construct the intelligent integrated surveillance system, we describe the method not to focus on the video of each camera but to aware the whole event by sharing information between cameras, which is more accurate. The method would be used to aware the event in the fixed area such as stations in urban transit.

• Journal of Korea Multimedia Society
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• v.17 no.4
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• pp.420-432
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• 2014

In detection of moving objects from video sequences, an essential process for intelligent visual surveillance, the cast shadows accompanying moving objects are different from background so that they may be easily extracted as foreground object blobs, which causes errors in localization, segmentation, tracking and classification of objects. Most of the previous research results about moving cast shadow detection and removal usually utilize color information about objects and scenes. In this paper, we proposes a novel cast shadow removal method of moving objects in gray level video data for visual surveillance application. The proposed method utilizes observations about edge patterns in the shadow region in the current frame and the corresponding region in the background scene, and applies Laplacian edge detector to the blob regions in the current frame and the corresponding regions in the background scene. Then, the product of the outcomes of application determines moving object blob pixels from the blob pixels in the foreground mask. The minimal rectangle regions containing all blob pixles classified as moving object pixels are extracted. The proposed method is simple but turns out practically very effective for Adative Gaussian Mixture Model-based object detection of intelligent visual surveillance applications, which is verified through experiments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.172-179
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• 2015

Because of the development of computers and high-technology applications, all devices that we use have become more intelligent. In recent years, security and surveillance systems have become more complicated as well. Before new technologies included video surveillance systems, security cameras were used only for recording events as they occurred, and a human had to analyze the recorded data. Nowadays, computers are used for video analytics, and video surveillance systems have become more autonomous and automated. The types of security cameras have also changed, and the market offers different kinds of cameras with integrated software. Even though there is a variety of hardware, their capabilities leave a lot to be desired. Therefore, this drawback is trying to compensate by dint of computer program solutions. Image processing is a very important part of video surveillance and security systems. Capturing an image exactly as it appears in the real world is difficult if not impossible. There is always noise to deal with. This is caused by the graininess of the emulsion, low resolution of the camera sensors, motion blur caused by movements and drag, focus problems, depth-of-field issues, or the imperfect nature of the camera lens. This paper reviews image processing, pattern recognition, and image digitization techniques, which will be useful in security services, to analyze bio-images, for image restoration, and for object classification.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.417-428
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• 2018

The intelligent video surveillance environment is a system that extracts various information about a video object and enables automated processing through the analysis of video data collected in CCTV. However, since the privacy exposure problem may occur in the process of intelligent video surveillance, it is necessary to take a security measure. Especially, video metadata has high vulnerability because it can include various personal information analyzed based on big data. In this paper, we propose a COP-Transformation scheme to protect video metadata. The proposed scheme is advantageous in that it greatly enhances the security and efficiency in processing the video metadata.