• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.186-198
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• 2021

Security control using surveillance cameras is established when people observe all surveillance videos directly. However, this task is labor-intensive and it is difficult to detect all abnormal situations. In this paper, we propose a deep neural network model, called AT-Net, that automatically detects abnormal situations in the surveillance video, and introduces an automatic video surveillance system developed based on this network model. In particular, AT-Net alleviates the ambiguity of existing abnormal situation detection methods by mapping features representing relationships between people and objects in surveillance video to the new tensor structure based on sparse coding. Through experiments on actual surveillance videos, AT-Net achieved an F1-score of about 89%, and improved abnormal situation detection performance by more than 25% compared to existing methods.

• Journal of Satellite, Information and Communications
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• v.7 no.1
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• pp.33-38
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• 2012

In this paper we proposed an algorithm selection method based on the features of video inputs which are acquired from the large scale integrated surveillance system. The example of integrated surveillance system is the metro railway system. Automated surveillance system at large area saves the human resource and minimizes the non-observing spots. We have analyzed the input video under this system in order to apply adequate video analytics algorithms in each installing places and for each situation. Based on the analysis, we suggested event processing scenarios and video analytic algorithm selection.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.120-123
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• 2020

The surveillance system to prevent crime and accidents in advance has become a necessity, not an option in real life. Not only public institutions but also individuals are installing surveillance cameras to protect their property and privacy. However, since the installed surveillance camera cannot be monitored for 24 hours, the focus is on the technology that tracks the video after an accident occurs rather than prevention. In this paper, we propose a system model that monitors abnormal behaviors that may cause crimes through real-time video, and when a specific behavior occurs, the surveillance system automatically detects it and responds immediately through an alarm. We are a model that analyzes real-time images from surveillance cameras and uses I3D models from analysis servers to analyze abnormal behavior and deliver notifications to web servers and then to clients. If the system is implemented with the proposed model, immediate response can be expected when a crime occurs.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.169-177
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• 2014

It is essential to support the image and video analysis technology such as motion detection since the DVR and NVR storage were adopted in the real time visual surveillance system. Especially the network camera would be popular as a video input device. The traditional CCTV that supports analog video data get be replaced by the network camera. In this paper, we present the design and implementation of video surveillance system that provides the real time motion detection by the video storage server. The mobile application also has been implemented in order to provides the retrieval functionality of image analysis results. We develop the video analysis server with open source library OpenCV and implement the daemon process for video input processing and real-time image analysis in our video surveillance system.

• 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.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.306-309
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• 2008

A system realization for video surveillance with interframe probability distribution analysis is presented in this paper. The system design is based on a high performance DSP processor, video surveillance is implemented by analyzing interframe probability distribution for scanning objects in a restricted area and the video analysis algorithm is decided for forming a different image from the probability distribution of several frames compressed by the standardized JPEG. The algorithm processing time of D1($720{\times}480$) image per frame is 85ms.

• Convergence Security Journal
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• v.15 no.3_2
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• pp.21-30
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• 2015

Current video surveillance system that is being used in the country are composed of different video surveillance s ystem that is produced in a number of domestic and foreign manufacturers and perform a service. These video surv eillance systems are using different protocols and interfaces, operating method, it provides such performance without interworking and interoperability between the video surveillance systems, in accordance with the purpose of operatin g the subject is being operated separately. Therefore, in order to provide an image with a more systematic and effici ent security service, many operational constraints are requested. The present study defines the standardization for a unified standard interfaces and protocols to ensure the interworking and interoperability between the different individ ual video security system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.6
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• pp.1064-1069
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• 2008

A system design and realization for video surveillance with interframe probability distribution analyzation is presented in this paper. The system design is based on a high performance DSP professor, video surveillance is implemented by analyzing interframe probability distribution using trivariate normal distribution(weight, mean, variance) for scanning objects in a restricted area and the video analysis algorithm is decided for forming a different image from the probability distribution of several frame compressed by the standardized JPEG. The system processing time of D1$(720{\times}480)$ image per frame is 85ms and enables to process the system at 12 frames per second. An object surveillance about the restricted area by rules is extracted to 100% unless object is moved faster.

• Annual Conference of KIPS
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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.