• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.693-698
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• 2018

This paper proposes a vehicle detection method for parking areas using unmanned aerial vehicles (UAVs) and using YOLOv2, which is a recent, known, fast, object-detection real-time algorithm. The YOLOv2 convolutional network algorithm can calculate the probability of each class in an entire image with a one-pass evaluation, and can also predict the location of bounding boxes. It has the advantage of very fast, easy, and optimized-at-detection performance, because the object detection process has a single network. The sliding windows methods and region-based convolutional neural network series detection algorithms use a lot of region proposals and take too much calculation time for each class. So these algorithms have a disadvantage in real-time applications. This research uses the YOLOv2 algorithm to overcome the disadvantage that previous algorithms have in real-time processing problems. Using Darknet, OpenCV, and the Compute Unified Device Architecture as open sources for object detection. a deep learning server is used for the learning and detecting process with each car. In the experiment results, the algorithm could detect cars in a dense area using UAVs, and reduced overhead for object detection. It could be applied in real time.

• 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.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1843_1844
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• 2009

본 논문에서는 영상처리 기술을 기반으로 한 내부 운전자에게 위협이 될 수 있는 금속 물체를 탐지하기 위한 실시간 시스템을 제안한다. 제안된 시스템은 퍼지 이론을 이용하여 금속물체를 탐지할 수 있는 색상 필터를 설계하여 사용하였다. 차량안의 특정 탐지 영역 내에서 FCF(Fuzzy Skin Filter)를 이용하여 운전자의 얼굴 영역을 탐지하고, 동승자가 위협을 가한다는 가정 하에 손 영역을 탐지한다. 탐지된 동승자의 손 영역을 중심으로 색상기반 원형 탐색기법을 사용하여 최종 금속물체의 후보영역을 설정하고, 금속물체 색상필터를 적용하여 최종적인 금속물체영역을 탐지 한다. 제안된 방법은 여러 실험을 통해 내부 운전자 보호를 위한 금속물체 탐지 시스템의 우수성을 증명한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1262-1270
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• 2020

Recently, the smart video surveillance research, which has been receiving increasing attention, has mainly focused on the intruder detection and tracking, and abandoned object detection. On the other hand, research on real-time detection of stolen objects is relatively insufficient compared to its importance. Considering various smart surveillance video application environments, this paper presents two different types of stolen object detection algorithms. We first propose an algorithm that detects theft of statically and dynamically registered surveillance objects using a dual background subtraction model. In addition, we propose another algorithm that detects theft of general surveillance objects by applying the dual background subtraction model and Mask R-CNN-based object segmentation technology. The former algorithm can provide economical theft detection service for pre-registered surveillance objects in low computational power environments, and the latter algorithm can be applied to the theft detection of a wider range of general surveillance objects in environments capable of providing sufficient computational power.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.609-614
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• 2009

The purpose of this paper is to design the metal object detection system for drive inside protection. To do this, we propose the algorithm for designing the color filter that can detect the metal object using fuzzy theory and the algorithm for detecting area of the driver's face using fuzzy skin color filter. Also, by using the proposed algorithm, we propose the algorithm for detecting the metallic object candidate regions. And, the metallic object color filter is then applied to find the candidate regions. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• Geophysics and Geophysical Exploration
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• v.22 no.2
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• pp.72-79
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• 2019

Underwater object detection method adopting electrical resistivity technique was proposed recently, and the need of advanced data processing algorithm development counteracting various marine environmental conditions was required. In this paper, we present an improved water tank experiment system and its operation results, which can provide efficient test and verification. The main features of the system are as follows: 1) All the processes enabling real time process for not only simultaneous gathering of object images but also the electrical field measurement and visualization are carried out at 5 Hz refresh rates. 2) Data acquisition and processing for two detection lines are performed in real time to distinguish the moving direction of a target object. 3) Playback and retest functions for the saved data are equipped. 4) Through the monitoring screen, the movement of the target object and the measurement status of two detection lines can be intuitively identified. We confirmed that the enhanced physical modeling system works properly and facilitates efficient experiments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.2C
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• pp.181-187
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• 2012

Millimeter wave (MMW) readily penetrates fabrics, thus it can be used to detect objects concealed under clothing. A passive MMW imaging system can operate as a stand-off type sensor that scans people in both indoors and outdoors. However, because of the diffraction limit and low signal level, the imaging system often suffers from low image quality. Therefore, suitable statistical analysis and computational processing would be required for automatic analysis of the images. In this paper, a real-time concealed object detection is addressed by means of the multi-level segmentation. The histogram of the image is modeled with a Gaussian mixture distribution, and hidden object areas are segmented by a multi-level scheme involving $k$-means, the expectation-maximization algorithm, and a decision rule. The complete algorithm has been implemented in C++ environments on a standard computer for a real-time process. Experimental and simulation results confirm that the implemented system can achieve the real-time detection of concealed objects.

• 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.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.90-95
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• 2012

Remotely sensed data provide valuable information on land monitoring due to multi-temporal observation over large areas. Especially, high resolution imagery with 0.6~1.0 m spatial resolutions contain a wealth of information and therefore are very useful for thematic mapping and monitoring change in urban areas. Recently, remote sensing technology has been successfully utilized for natural disaster monitoring such as forest fire, earthquake, and floods. In this paper, an efficient change detection method based on texture differences observed from high resolution multi-temporal data sets is proposed for mapping disaster damage and extracting damage information. It is composed of two parts: feature extraction and detection process. Timely and accurate information on disaster damage can provide an effective decision making and response related to damage.

• 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.