• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.5
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• pp.434-439
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• 2013

It is very difficult to monitor the blind spots that are not recognized by traditional surveillance camera (CCTV) systems, and the surveillance efficiencies are very low though many accidents/events can be solved by the systems. In this paper, the fuzzy logic based sound source localization system using sound strength in the underground parking lot is suggested and the performance of the system is analyzed in order to enhance the stabilization and the accuracy of the localization algorithm in the suggested system. It is confirmed that the localization stabilization of the localization algorithm (SLA_fuzzy) using the fuzzy logic in the suggested system is 4 times higher than that of the conventional localization algorithm (SLA). In addition to this, the localization accuracy of the SLA_fuzzy in the suggested system is 29% higher than that of the SLA.

• Journal of Sensor Science and Technology
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• v.11 no.1
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• pp.28-38
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• 2002

People are important targets for video surveillance and monitoring(VSAM) but difficult to be analyzed. In this paper, a technique to count people in image sequences is dealt as a prerequisite procedure for automatic tracking and behaviour analysis. A group of people is divided at local minima of the line connecting the highest pixels on the binary image of the people extracted from the image taken by a stationary video camera. As the properties of the divided regions vary according to the relative positions of the people in a group, different states are assigned for the completely occluded, partially occluded, completed separated individual, and wrongly divided regions. By analyzing the transition of the states of divided regions, the number of people on the site monitored is estimated. The technique is checked in real experimental situations.

• Journal of IKEEE
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• v.24 no.4
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• pp.1148-1155
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• 2020

Fire must be extinguished as quickly as possible because they cause a lot of economic loss and take away precious human lives. Especially, the detection of smoke, which tends to be found first in fire, is of great importance. Smoke detection based on image has many difficulties in algorithm research due to the irregular shape of smoke. In this study, we introduce a new real-time smoke detection algorithm that reduces the detection of false positives generated by irregular smoke shape based on faster r-cnn of factory-installed surveillance cameras. First, we compute the global frame similarity and mean squared error (MSE) to detect the movement of smoke from the input surveillance camera. Second, we use deep learning algorithm (Faster r-cnn) to extract deferred candidate regions. Third, the extracted candidate areas for acting are finally determined using space and temporal features as smoke area. In this study, we proposed a new algorithm using the space and temporal features of global and local frames, which are well-proposed object information, to reduce false positives based on deep learning techniques. The experimental results confirmed that the proposed algorithm has excellent performance by reducing false positives of about 99.0% while maintaining smoke detection performance.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.5
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• pp.93-98
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• 2011

In this paper, we propose the surveillance and combat robot framework for remote monitoring and robot control on the smart phone, which is implemented with the fusion technology called RITS(Robot technology & Information Technology System). In our implemented system, the camera phone mounted on the robot generates signals to control the robot and sends images to the smart phone of the operator. Therefore, we can monitor the surrounding area of the robot with the smart phone. Besides the control of the movement of the robot, we can fire the weapons armed on the robot by sending the fire command. From experimental results, we can conclude that it's possible to control the robot and monitor the surrounding area of the robot and fire the weapons in real time in the region where the 3G(Generation) mobile communication is possible. In addition, we controlled the robot using the 2G mobile communication or wired phone when the robot is in the visual range.

• Informatization Policy
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• v.25 no.2
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• pp.3-19
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• 2018

With the introduction of video cameras into law enforcement, a great deal of police organizations have adopted the technology in their routine crime prevention activities. The up-to-date systems of ambient surveillance energized by CCTV, police wearable cameras, drones, and thermal imaging devices enable the police to thoroughly monitor public spaces as well as to rigorously arrest on-scene criminals. These efforts to improve the level of surveillance are often met with public resistance raising concerns over citizens' rights to privacy. Recent studies on the use of police video equipment have constantly raised the issues related to the lack of applicable legal provisions, risk of personal information and privacy infringement as well as security vulnerabilities. In this regard, the present study attempted to review the public surveillance methods currently used by law enforcement agencies worldwide within the context of public safety and individual rights to privacy. Furthermore, the present study also discussed the legal boundaries of police use of video equipment to address public concerns over privacy issues.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.1
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• pp.13-19
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• 2011

In this paper, a visual surveillance system using SOPC based NIOS II embedded processor and C2H compiler was implemented. In this system, the IP is constructed by C2H compiler for the output of the camera images, image processing, serial communication and network communication, then, it is implemented to effectively control each IP based on the SOPC and the NIOS II embedded processor. And, an algorithm which updates the background images for high speed and robust detection of the moving objects is proposed using the Adaptive Gaussian Mixture Model(AGMM). In results, it can detecte the moving objects(pedestrians and vehicles) under day-time and night-time. It is confirmed that the proposed AGMM algorithm has better performance than the Adaptive Threshold Method(ATM) and the Gaussian Mixture Model(GMM) from our experiments.

• 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.

• The Journal of Korean Institute of Information Technology
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• v.17 no.5
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• pp.105-116
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• 2019

Most of the public and private buildings in Korea are installing CCTV for crime prevention and follow-up action, insider security, facility safety, and fire prevention, and the number of installations is increasing each year. In the questionnaire conducted on the increasing CCTV, many reactions were positive in terms of the prevention of crime that could occur due to the installation, rather than negative views such as privacy violation caused by CCTV shooting. However, CCTV poses a lot of privacy risks, and when the image data is collected using the cloud, the personal information of the subject can be leaked. InseCam relayed the CCTV surveillance video of each country in real time, including the front camera of the notebook computer, which caused a big issue. In this paper, we introduce a system to prevent leakage of private information and enhance the security of the cloud system by processing the privacy technique on image information about a subject photographed through CCTV.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4292-4307
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• 2021

For the research field of night infrared surveillance video, the target imaging in the video is easily affected by the light due to the characteristics of the active infrared camera and the classical ViBe algorithm has some problems for moving target extraction because of background misjudgment, noise interference, ghost shadow and so on. Therefore, an improved ViBe algorithm (I-ViBe) for moving target extraction in night infrared surveillance video is proposed in this paper. Firstly, the video frames are sampled and judged by the degree of light influence, and the video frame is divided into three situations: no light change, small light change, and severe light change. Secondly, the ViBe algorithm is extracted the moving target when there is no light change. The segmentation factor of the ViBe algorithm is adaptively changed to reduce the impact of the light on the ViBe algorithm when the light change is small. The moving target is extracted using the region growing algorithm improved by the image entropy in the differential image of the current frame and the background model when the illumination changes drastically. Based on the results of the simulation, the I-ViBe algorithm proposed has better robustness to the influence of illumination. When extracting moving targets at night the I-ViBe algorithm can make target extraction more accurate and provide more effective data for further night behavior recognition and target tracking.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.7
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• pp.488-498
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• 2007

This paper presents a novel approach for representing the spatio-temporal topology of the camera network with overlapping and non-overlapping fields of view (FOVs) in Ubiquitous Smart Space (USS). The topology is determined by tracking moving objects and establishing object correspondence across multiple cameras. To track people successfully in multiple camera views, we used the Merge-Split (MS) approach for object occlusion in a single camera and the grid-based approach for extracting the accurate object feature. In addition, we considered the appearance of people and the transition time between entry and exit zones for tracking objects across blind regions of multiple cameras with non-overlapping FOVs. The main contribution of this paper is to estimate transition times between various entry and exit zones, and to graphically represent the camera topology as an undirected weighted graph using the transition probabilities.