• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.956-959
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• 2018

각종 테러 및 범죄 예방을 위한 지능형 영상분석 시스템을 구현하는데 기본적으로는 로컬 기반의 처리를 진행하고 필요에 따라 클라우드 기반의 처리가 병렬적으로 처리됨과 동시에 기존의 시스템에서 추가적인 구축, 설비비용 없이 설치되어있던 DVR 또는 NVR 장치를 활용하여 간단한 소프트웨어를 추가함으로서 영상분석 서비스를 이용 가능하도록 하여 영상분석 결과에 대한 신뢰성이 높고 비용 절약적인 하이브리드 지능형 침입 감지 시스템을 제안.

• Journal of Satellite, Information and Communications
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• v.11 no.4
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• pp.1-9
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• 2016

This paper was studied in two ways with respect to the information received from the video signal separation technique of PPI Scop radar device. The proposed technique consists in generating an image signal through the video signal separation and synthesis, symbol generation, the residual image signal generation process. This technology can greatly improve the operating convenience with improved ease of discrimination, screen readability for the operator in analyzing radar information. The first proposed method was constructed for high-speed FPGA-based information processing systems for high speed operation stability of the system. The second proposed method was implemented intelligent algorithms and a software algorithm function curve associated resources.This was required to meet the constraints on the radar information, analysis system. Existing radar systems have not the frame data analysis unit image. However, this study was designed to image data stored in the frame-by-frame analysis of radar images with express information MPEG4 video. Key research content is to highlight the key observations expresses the target, the object-specific monitoring information to the positive image processing algorithm and the function curve delays. For high-definition video, high-speed to implement data analysis and expressing a variety of information was applied to the ARM Processor Support in Pro ASIC3.

• International Journal of High-Rise Buildings
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• v.12 no.3
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• pp.225-234
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• 2023

Even at this point in the era of digital transformation, we are still facing many problems in the safety sector that cannot prevent the occurrence or spread of human casualties. When you are in an unexpected emergency, it is often difficult to respond only with human physical ability. Human casualties continue to occur at construction sites, manufacturing plants, and multi-use facilities used by many people in everyday life. If you encounter a situation where normal judgment is impossible in the event of an emergency at a life site where there are still many safety blind spots, it is difficult to cope with the existing manual guidance method. New variable guidance technology, which combines artificial intelligence and digital twin, can make it possible to prevent casualties by processing large amounts of data needed to derive appropriate countermeasures in real time beyond identifying what safety accidents occurred in unexpected crisis situations. When a simple control method that divides and monitors several CCTVs is digitally converted and combined with artificial intelligence and 3D digital twin control technology, intelligence augmentation (IA) effect can be achieved that strengthens the safety decision-making ability required in real time. With the enforcement of the Serious Disaster Enterprise Punishment Act, the importance of distributing a smart location guidance system that urgently solves the decision-making delay that occurs in safety accidents at various industrial sites and strengthens the real-time decision-making ability of field workers and managers is highlighted. The smart location guidance system that combines artificial intelligence and digital twin consists of AIoT HW equipment, wireless communication NW equipment, and intelligent SW platform. The intelligent SW platform consists of Builder that supports digital twin modeling, Watch that meets real-time control based on synchronization between real objects and digital twin models, and Simulator that supports the development and verification of various safety management scenarios using intelligent agents. The smart location guidance system provides on-site monitoring using IoT equipment, CCTV-linked intelligent image analysis, intelligent operating procedures that support workflow modeling to immediately reflect the needs of the site, situational location guidance, and digital twin virtual fencing access control technology. This paper examines the limitations of traditional fixed passive guidance methods, analyzes global technology development trends to overcome them, identifies the digital transformation properties required to switch to intelligent variable smart location guidance methods, explains the characteristics and components of AI-based public facility smart fire safety integrated system (ISFS).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.11-22
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• 2010

This paper is a study on implementation of intelligent image surveillance system using context information and supplements temporal-spatial constraint, the weak point in which it is hard to process it in real time. In this paper, we propose scene analysis algorithm which can be processed in real time in various environments at low resolution video(320*240) comprised of 30 frames per second. The proposed algorithm gets rid of background and meaningless frame among continuous frames. And, this paper uses wavelet transform and edge histogram to detect shot boundary. Next, representative key-frame in shot boundary is selected by key-frame selection parameter and edge histogram, mathematical morphology are used to detect only motion region. We define each four basic contexts in accordance with angles of feature points by applying vertical and horizontal ratio for the motion region of detected object. These are standing, laying, seating and walking. Finally, we carry out scene analysis by defining simple context model composed with general context and emergency context through estimating each context's connection status and configure a system in order to check real time processing possibility. The proposed system shows the performance of 92.5% in terms of recognition rate for a video of low resolution and processing speed is 0.74 second in average per frame, so that we can check real time processing is possible.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.512-517
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• 2011

In this paper, we propose an Active Shape Model (ASM) and Lucas-Kanade (LK) optical flow-based feature extraction and analysis method for analyzing the emotional features from facial images. Considering the facial emotion feature regions are described by Facial Action Coding System, we construct the feature-related shape models based on the combination of landmarks and extract the LK optical flow vectors at each landmarks based on the centre pixels of motion vector window. The facial emotion features are modelled by the combination of the optical flow vectors and the emotional states of facial image can be estimated by the probabilistic estimation technique, such as Bayesian classifier. Also, we extract the optimal emotional features that are considered the high correlation between feature points and emotional states by using common spatial pattern (CSP) analysis in order to improvise the operational efficiency and accuracy of emotional feature extraction process.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.62-68
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• 2012

Steady-State Visually Evoked Potentials (SSVEP) are natural response signal associated with the visual stimuli with specific frequency. By using SSVEP, occipital lobe region is electrically activated as frequency form equivalent to stimuli frequency with bandwidth from 3.5Hz to 75Hz. In this paper, we propose an experimental paradigm for analyzing EEGs based on the properties of SSVEP. At first, an experiment is performed to extract frequency feature of EEGs that is measured from the image-based visual stimuli associated with specific objective with affordance and object-related affordance is measured by using mirror neuron system based on the frequency feature. And then, linear discriminant analysis (LDA) method is applied to perform the online classification of the objective pattern associated with the EEG-based affordance data. By using the SSVEP measurement experiment, we propose a Brain-Computer Interface (BCI) system for recognizing user's inherent intentions. The existing SSVEP application system, such as speller, is able to classify the EEG pattern based on grid image patterns and their variations. However, our proposed SSVEP-based BCI system performs object pattern classification based on the matters with a variety of shapes in input images and has higher generality than existing system.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.155-164
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• 2004

Recently, we have remarkable developments in intelligent robot systems. The remarkable features of intelligent robot are that it can track user and is able to doface recognition, which is vital for many surveillance-based systems. The advantage of face recognition compared with other biometrics recognition is that coerciveness and contact that usually exist when we acquire characteristics do not exist in face recognition. However, the accuracy of face recognition is lower than other biometric recognition due to the decreasing in dimension from image acquisition step and various changes associated with face pose and background. There are many factors that deteriorate performance of face recognition such as thedistance from camera to the face, changes in lighting, pose change, and change of facial expression. In this paper, we implement a new sliding active camera system to prevent various pose variation that influence face recognition performance andacquired frontal face images using PCA and HMM method to improve the face recognition. This proposed face recognition algorithm can be used for intelligent surveillance system and mobile robot system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2180-2188
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• 2009

This paper is study on real-time hand gesture recognition system based on vision for intelligent robot control. We are proposed a recognition system using PCA and BP algorithm. Recognition of hand gestures consists of two steps which are preprocessing step using PCA algorithm and classification step using BP algorithm. The PCA algorithm is a technique used to reduce multidimensional data sets to lower dimensions for effective analysis. In our simulation, the PCA is applied to calculate feature projection vectors for the image of a given hand. The BP algorithm is capable of doing parallel distributed processing and expedite processing since it take parallel structure. The BP algorithm recognized in real time hand gestures by self learning of trained eigen hand gesture. The proposed PCA and BP algorithm show improvement on the recognition compared to PCA algorithm.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.6
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• pp.1-9
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• 2014

Recently, a study is conducting to image collection and auto detection of attribute information using mobile mapping system. The road sign attribute information detection is difficult because of various size and placement, interference of other facilities like trees. In this study, a text detection method that does not rely on a Korean character template is required to successfully detect the target text when a variety of differently sized texts are present near the target texts. To overcome this, the method of incremental right-to-left blob projection (IRBP) was suggested as a solution; the potential and improvement of the method was also assessed. To assess the performance improvement of the IRBP that was developed, the IRBP method was compared to the existing method that uses Korean templates through the 60 videos of street signs that were used. It was verified that text detection can be improved with the IRBP method.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.343-348
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• 2014

Face recognition system has advanctage to automatically recognize a person without causing repulsion at deteciton process. However, the face recognition system has a drawback to show lower perfomance according to illumination variation unlike the other biometric systems using fingerprint and iris. Therefore, this paper proposed a robust face recogntion method against illumination varition by slective fusion technique using both visible and infrared faces based on fuzzy linear disciment analysis(fuzzy-LDA). In the first step, both the visible image and infrared image are divided into four bands using wavelet transform. In the second step, Euclidean distance is calculated at each subband. In the third step, recognition rate is determined at each subband using the Euclidean distance calculated in the second step. And then, weights are determined by considering the recognition rate of each band. Finally, a fusion face recognition is performed and robust recognition results are obtained.