• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.2
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• pp.153-162
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• 2014

As CCTVs are widely utilized in diverse fields, many researchers have continuously studied to improve the surveillance performances of a CCTV system. However, an quantitative evaluation approach about the surveillance performance has rarely been researched. Therefore, we set up the research for suggesting a quantitative evaluation approach to determine the effectiveness of CCTV coverages. We firstly defined the surveillance resolution as that varies according to object's positions and orientations. Based on the definition, we computed surveillance resolution values at all three-dimensional positions with the orientations of interests in the specified space. By comparing these values to the required reasonable resolution, we determined the surveillance performance index indicating how well a CCTV system monitor a target space for specific surveillance objectives. This proposed approach evaluates the surveillance performance of a CCTV system quantitatively, so as examines the CCTV system design before its installation based on precise 3D spatial analysis.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.317-320
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• 2003

In this paper, we present real-time, accurate face region detection and tracking technique for an intelligent surveillance system. It is very important to obtain the high-resolution images, which enables accurate identification of an object-of-interest. Conventional surveillance or security systems, however, usually provide poor image quality because they use one or more fixed cameras and keep recording scenes without any clue. We implemented a real-time surveillance system that tracks a moving person using pan-tilt-zoom (PTZ) cameras. While tracking, the region-of-interest (ROI) can be obtained by using a low-pass filter and background subtraction. Color information in the ROI is updated to extract features for optimal tracking and zooming. The experiment with real human faces showed highly acceptable results in the sense of both accuracy and computational efficiency.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.25-30
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• 2012

It is important to detect dangerous objects which are intentionally abandoned in public places. Nowadays visual surveillance system is required to enhance the performance in two ways : high resolution and wireless linking ability. In this study the design of visual surveillance system is newly proposed to detect abandoned objects for social security purpose based on wireless high resolution image transmission technology. Also, to enhance PED, PAT performance, the tracking algorithm is included in the previous visual surveillance software scheme. By implementing proposed design scheme on the real wireless high resolution image transmission system, the effectiveness of the overall system is shown with the transmission performance of 4.0 Gbps speed.

• Journal of IKEEE
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• v.18 no.4
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• pp.612-619
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• 2014

This paper proposes a real-time multi-camera video surveillance system with automatic resolution control using motion detection. In ordinary times, it acquires 4 channels of QVGA images, and it merges them into single VGA image and transmit it. When motion is detected, it automatically increases the resolution of motion-occurring channel to VGA and decreases those of 3 other channels to QQVGA, and then these images are overlaid and transmitted. Thus, it can magnifies and watches the motion-occurring channel while maintaining transmission bandwidth and monitoring all other channels. When it is synthesized with 0.18 um technology, the maximum operating frequency is 110 MHz, which can theoretically support 4 HD cameras.

• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.105-108
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• 2020

This paper presents a real-time low-resolution face recognition method that uses a super-resolution technique. Conventional face recognition methods are limited by low accuracy resulting from the distance between the camera and objects. Although super-resolution methods have been developed to resolve this issue, they are not suitable for integrated face recognition systems. The proposed method recognizes faces with low resolution using key frame selection, super resolution, face detection, and recognition on real-time processing. Experiments involving several databases indicated that the proposed algorithm is superior to conventional methods in terms of face recognition accuracy.

• 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 and Communication Engineering
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• v.26 no.12
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• pp.1816-1825
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• 2022

In this paper, we propose a new approach for tracking each human in a surveillance camera network with various resolution cameras. When tracking human on multiple non-overlapping cameras, the traditional appearance features are easily affected by various camera viewing conditions. To overcome this limitation, the proposed system utilizes facial information along with appearance information. In general, human images captured by the surveillance camera are often low resolution, so it is necessary to be able to extract useful features even from low-resolution faces to facilitate tracking. In the proposed tracking scheme, texture-based face descriptor is exploited to extract features from detected face after face frontalization. In addition, when the size of the face captured by the surveillance camera is very small, a super-resolution technique that enlarges the face is also exploited. The experimental results on the public benchmark Dana36 dataset show promising performance of the proposed algorithm.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.659-669
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• 2015

Face Recognition assumes much significance in the context of security based application. Normally, high resolution images offer more details about the image and recognizing a face from a reasonably high resolution image would be easier when compared to recognizing images from very low resolution images. This paper addresses the problem of recognizing faces from a very low resolution image whose size is as low as $8{\times}8$. With the use of CCTV(Closed Circuit Television) and with other surveillance camera-based application for security purposes, the need to overcome the shortcomings with very low resolution images has been on the rise. The present day face recognition algorithms could not provide adequate performance when employed to recognize images from VLR images. Existing methods use super-resolution (SR) methods and Relation Based Super Resolution methods to construct from very low resolution images. This paper uses a learning based super resolution method to extract and construct images from very low resolution images. Experimental results show that the proposed SR algorithm based on relationship learning outperforms the existing algorithms in public face databases.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.197-200
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• 2005

Super resolution image reconstruction method refers to image processing algorithms that produce a high resolution(HR) image from observed several low resolution(LR) images of the same scene. This method is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, such as satellite imaging, video surveillance, video enhancement and restoration, digital mosaicking, and medical imaging. In this paper we applied super resolution reconstruction method in spatial domain to video sequences. Test images are adjacently sampled images from continuous video sequences and overlapped for high rate. We constructed the observation model between the HR images and LR images applied by the Maximum A Posteriori(MAP) reconstruction method that is one of the major methods in the super resolution grid construction. Based on this method, we reconstructed high resolution images from low resolution images and compared the results with those from other known interpolation methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.628-631
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• 2003

In this paper, we present real-time, accurate face region detection and tracking technique for an intelligent surveillance system. It is very important to obtain the high-resolution images, which enables accurate identification of an object-of-interest. Conventional surveillance or security systems, however, usually provide poor image quality because they use one or more fixed cameras and keep recording scenes without any cine. We implemented a real-time surveillance system that tracks a moving person using four pan-tilt-zoom (PTZ) cameras. While tracking, the region-of-interest (ROI) can be obtained by using a low-pass filter and background subtraction. Color information in the ROI is updated to extract features for optimal tracking and zooming. The experiment with real human faces showed highly acceptable results in the sense of both accuracy and computational efficiency.