• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.62-65
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• 2003

본 연구는 탐지대상물체의 형상인식이 가능한 비접촉, 원격 탐지장치의 개발에 관한 것이다. 본 연구에서는 2극 또는 4극의 정자기장을 인가할 때 탐지대상 물체에 의한 자기장의 자계 왜곡을 홀 센서로 감지하여 탐지 대상 물체를 인식하는 원격 감지 시스템을 설계하고 제작하여 특성을 분석한 결과 2극과 4극 시스템 모두 비 투자율의 변화를 감지 할 수 있었고, 특히 탐사물체의 위치 파악에는 2극 시스템이 4극 시스템에 비하여 좋은 특성을 보였다.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.289-298
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• 2009

Active Contour Model, that is, Snake algorithm is effective for detection and tracking the objects. However, this algorithm has some drawbacks; numerous parameters must be designed(weighting factors, iteration steps, etc.), a reasonable initialization must be available and moreover suffers from numerical instability. Therefore we propose a novel Energy Corrected Snake(ECS) algorithm which improved on external energy of Snake algorithm for detection and tracking the moving object more effectively. The proposed algorithm uses the difference image, getting when the object is moving. It copies four direction images from the difference image and performs the accumulating compute to erasing image noise, so that it gets external energy steadily. Then external energy united with contour that is computed by internal energy. Consequently we can detect and track the moving object more speedily and easily. To show the effectiveness of the proposed algorithm, we experiment on 3 situations. The experimental results showed that the proposed algorithm outperformed by 6$\sim$9% of detection rate and 6$\sim$11% of tracker detection rate compared with the Snake algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.421-423
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• 2002

본 논문에서는 실시간에 강체 운동을 하는 일반적인 모델사이의 효율적인 충돌검사 알고리즘을 제안한다. 기존의 경계볼륨 알고리즘에 계층적 구조를 적용하였다. 이는 볼록한 물체를 위한 보로노이 영역 기반의 충돌검사 알고리즘을 오목한 물체에도 적용할 수 있도록 확장한다. 추가적으로 빠르게 움직이는 물체에 대한 관통을 탐지하기 위해서 물체의 이동 경로에 대한 교차 검사를 진행한다. 구현된 알고리즘은 일반적인 응용에서 기대한 성능 향상을 얻을 수 있다.

• Journal of the Korea Society of Computer and Information
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• v.20 no.4
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• pp.57-67
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• 2015

The concept of detection and classification of objects based on infrared camera is widely applied to military applications. While the object detection technology using infrared images has long been researched and the latest one can detect the object in sub-pixel, the object classification technology still needs more research. In this paper, we present object classification method based on measured radiant intensity of objects such as target, artillery, and missile using infrared camera. The suggested classification method was verified by radiant intensity measuring experiment using black body. Also, possible measuring errors were compensated by modelling-based correction for accurate radiant intensity measure. After measuring radiation of object, the model of radiant intensity is standardized based on theoretical background. Based on this research, the standardized model can be applied to the object classification by comparing with the actual measured radiant intensity of target, artillery, and missile.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.5
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• pp.195-204
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• 2018

In this paper, we propose both a new loss function and an online hard example mining scheme for improving the performance of single-stage object detectors which use deep convolutional neural networks. The proposed loss function and the online hard example mining scheme can not only overcome the problem of imbalance between the number of annotated objects and the number of background examples, but also improve the localization accuracy of each object. Therefore, the loss function and the mining scheme can provide intrinsically fast single-stage detectors with detection performance higher than or similar to that of two-stage detectors. In experiments conducted with the PASCAL VOC 2007 benchmark dataset, we show that the proposed loss function and the online hard example mining scheme can improve the performance of single-stage object detectors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.6
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• pp.1304-1311
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• 2003

Target tracking in a real world situation is difficult problem because of continuous variations in images, huge amounts of data, and high processing speed demands. The problem becomes even harder in the case of sea background. This paper presents an initial study of neural network based method for target detection and tracking in cluttering environment. The approach uses a combination of differential motion analysis, Kohonen self-organizing network and region growing method. The network is capable of detecting the mass-centers of moving objects within one frame. The history of neurons positions in the sequential frames approximates the traces of the targets. The experiments done with the network in simulated environment showed promising results.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06d
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• pp.119-122
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• 2007

무선 센서 네트워크에서 이동하는 물체 추적 실패시 이를 복구하기 위하여 많은 센서들의 에너지를 소비하기 때문에 이동 물체 추적 복구는 전체 센서 네트워크의 생명주기 연장에 중요한 요소이다. 본 논문에서는 물체의 이동정보를 고려한 동적 클러스터링 환경에서 이동물체의 추적 실패시 이동물체를 효율적으로 재 탐지할 수 있는 이동물체 추적 복구 기법을 제안함으로써 이동하는 물체추적 실패후 재 탐지에 성공하는 복구율을 증가시켜서 센서 노드의 에너지 소모를 최소화 하여 전체 센서 네트워크의 생명주기를 연장시키고자 한다. 시뮬레이션 결과가 증명하는 바와 같이 제안한 방식은 보다 높은 복구율을 달성하였다.

• Journal of Advanced Navigation Technology
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• v.24 no.3
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• pp.192-197
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• 2020

Recently, the technologies related to autonomous drive has studying the goal for safe operation and prevent accidents of vehicles. There is radar and camera technologies has used to detect obstacles in these autonomous vehicle research. Now a day, the method for using LiDAR sensor has considering to detect nearby objects and accurately measure the separation distance in the autonomous navigation. It is calculates the distance by recognizing the time differences between the reflected beams and it allows precise distance measurements. But it also has the disadvantage that the recognition rate of object in the atmospheric environment can be reduced. In this paper, point cloud data by triangular functions and Line Regression model are used to implement measurement algorithm, that has improved detecting objects in real time and reduce the error of measuring separation distances based on improved reliability of raw data from LiDAR sensor. It has verified that the range of object detection errors can be improved by using the Python imaging library.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.4 s.119
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• pp.455-463
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• 2007

In this paper, it is proposed how to implement the object detection system which is able to apply to vehicular applications, unmanned facilities, automatic door and others with microwave. As the technology which detects an object with microwave is becoming more popular, it seems impossible to avoid mutual interference and jamming caused by limited frequency bandwidth. The system in this paper detects an object by correlating the code of TX and RX signals with the pseudo-random code having best quality in interference and jamming environment. In order to generate simulant doppler signal for detecting the distance of an fixed object where there is no doppler effect, the phase of TX signal is shifted continually. Also, the saturation of receiver was removed and the error of distance measurement was decreased by controlling the power of TX signal for getting constant RX signal. The proposed system detects a object which ranges from 0.5 m to 2.0 m and informs vocally whether there is the object within 1.0 m or not.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1802-1808
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• 2003

In this paper, we propose a method about the extraction of the pedestrian tracking trajectory in the road and we used the method of mosaic background extraction and adaptive neural network for automatic pedestrian tracking system. We used mosaic background extraction to overcome ghost phenomenon. And we detected pedestrian using differential image analysis. We used adaptive neural network for multiple pedestrian tracking that non­rigid form moving. The ART2 network is capable of detecting the mass­centers of moving objects within one frame. The history of neurons positions in the sequential frames approximates the traces of the targets. The experiments done with the network in simulated environment show promising results.