• Journal of Astronomy and Space Sciences
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• v.36 no.3
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• pp.169-180
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• 2019

In this study, a batch least square estimator that utilizes optical observation data is developed and utilized to determine geostationary orbits (GEO). Through numerical simulations, the effects of error sources, such as clock errors, measurement noise, and the a priori state error, are analyzed. The actual optical tracking data of a GEO satellite, the Communication, Ocean and Meteorological Satellite (COMS), provided by the optical wide-field patrol network (OWL-Net) is used with the developed batch filter for orbit determination. The accuracy of the determined orbit is evaluated by comparison with two-line elements (TLE) and confirmed as proper for the continuous monitoring of GEO objects. Also, the measurement residuals are converged to several arcseconds, corresponding to the OWL-Net performance. Based on these analyses, it is verified that the independent operation of electro-optic space surveillance systems is possible, and the ephemerides of space objects can be obtained.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.8
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• pp.731-737
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• 2014

This paper presents abnormal behavior detection in crowd within surveillance video. We have defined below two cases as a abnormal behavior; first as a sporadically spread phenomenon and second as a sudden running in same direction. In order to detect these two abnormal behaviors, we first extract the motion vector and propose a new descriptor which is combined MHOF(Multi-scale Histogram of Optical Flow) and DCHOF(Directional Change Histogram of Optical Flow). Also, binary classifier SVM(Support Vector Machine) is used for detection. The accuracy of the proposed algorithm is evaluated by both UMN and PETS 2009 dataset and comparisons with the state-of-the-art method validate the advantages of our algorithm.

• Journal of Astronomy and Space Sciences
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• v.36 no.3
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• pp.187-197
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• 2019

The Optical Wide-field patroL-Network (OWL-Net) is a Korean optical space surveillance system used to track and monitor objects in space. In this study, the characteristics of four Initial Orbit Determination (IOD) methods were analyzed using artificial observational data from Low Earth Orbit satellites, and an appropriate IOD method was selected for use as the initial value of Precise Orbit Determination using OWL-Net data. Various simulations were performed according to the properties of observational data, such as noise level and observational time interval, to confirm the characteristics of the IOD methods. The IOD results produced via the OWL-Net observational data were then compared with Two Line Elements data to verify the accuracy of each IOD method. This paper, thus, suggests the best method for IOD, according to the properties of angles-only data, for use even when the ephemeris of a satellite is unknown.

• Journal of Astronomy and Space Sciences
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• v.28 no.4
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• pp.319-332
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• 2011

The objectives of this study are to analyze the satellite visibility at the randomly established ground sites, to determine the five optimal ground sites to perform the optical surveillance and tracking of domestic satellites, and to verify the acquisition of the optical observation time sufficient to maintain the precise ephemeris at optimal ground sites that have been already determined. In order to accomplish these objectives, we analyzed the visibility for sun-synchronous orbit satellites, low earth orbit satellites, middle earth orbit satellites and domestic satellites as well as the continuous visibility along with the fictitious satellite ground track, and calculate the effective visibility. For the analysis, we carried out a series of repetitive process using the satellite tool kit simulation software developed by Analytical Graphics Incorporated. The lighting states of the penumbra and direct sun were set as the key constraints of the optical observation. The minimum of the observation satellite elevation angle was set to be 20 degree, whereas the maximum of the sun elevation angle was set to be -10 degree which is within the range of the nautical twilight. To select the candidates for the optimal optical observation, the entire globe was divided into 84 sectors in a constant interval, the visibility characteristics of the individual sectors were analyzed, and 17 ground sites were arbitrarily selected and analyzed further. Finally, five optimal ground sites (Khurel Togoot Observatory, Assy-Turgen Observatory, Tubitak National Observatory, Bisdee Tier Optical Astronomy Observatory, and South Africa Astronomical Observatory) were determined. The total observation period was decided as one year. To examine the seasonal variation, the simulation was performed for the period of three days or less with respect to spring, summer, fall and winter. In conclusion, we decided the optimal ground sites to perform the optical surveillance and tracking of domestic satellites and verified that optical observation time sufficient to maintain the precise ephemeris could be acquired at the determined observatories.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.829-836
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• 2018

This study attempts to detect a shot boundary in films(or dramas) based on the length of a sequence. As films or dramas use scene change effects a lot, the issues regarding the effects are more diverse than those used in surveillance cameras, sports videos, medical care and security. Visual techniques used in films are focused on the human sense of aesthetic therefore, it is difficult to solve the errors in shot boundary detection with the method employed in surveillance cameras. In order to define the errors arisen from the scene change effects between the images and resolve those issues, the mixed algorithm based upon color histogram, edge histogram, and optical flow was implemented. The shot boundary data from this study will be used when analysing the configuration of meaningful shots in sequences in the future.

• Journal of Astronomy and Space Sciences
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• v.34 no.1
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• pp.19-30
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• 2017

The optical wide-field patrol network (OWL-Net) is a Korean optical surveillance system that tracks and monitors domestic satellites. In this study, a batch least squares algorithm was developed for optical measurements and verified by Monte Carlo simulation and covariance analysis. Potential error sources of OWL-Net, such as noise, bias, and clock errors, were analyzed. There is a linear relation between the estimation accuracy and the noise level, and the accuracy significantly depends on the declination bias. In addition, the time-tagging error significantly degrades the observation accuracy, while the time-synchronization offset corresponds to the orbital motion. The Cartesian state vector and measurement bias were determined using the OWL-Net tracking data of the KOMPSAT-1 and Cryosat-2 satellites. The comparison with known orbital information based on two-line elements (TLE) and the consolidated prediction format (CPF) shows that the orbit determination accuracy is similar to that of TLE. Furthermore, the precision and accuracy of OWL-Net observation data were determined to be tens of arcsec and sub-degree level, respectively.

• Journal of Astronomy and Space Sciences
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• v.28 no.3
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• pp.193-201
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• 2011

A strategy for geostationary orbit (or geostationary earth orbit [GEO]) surveillance based on optical angular observations is presented in this study. For the dynamic model, precise analytical orbit model developed by Lee et al. (1997) is used to improve computation performance and the unscented Kalman filer (UKF) is applied as a real-time filtering method. The UKF is known to perform well under highly nonlinear conditions such as surveillance in this study. The strategy that combines the analytical orbit propagation model and the UKF is tested for various conditions like different level of initial error and different level of measurement noise. The dependencies on observation interval and number of ground station are also tested. The test results shows that the GEO orbit determination based on the UKF and the analytical orbit model can be applied to GEO orbit tracking and surveillance effectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.103-110
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• 2012

The population growth along with the urbanization, has caused more problems in many public areas, such as subway airport terminals, hospital, etc. Many surveillance systems have been installed in the public areas, but not all of those can be monitored in real-time, because the operators that observe the monitors are very small compared with the number of the monitors. For example, the observer can miss some crucial accidents or detect after considerable delays. Thus, intelligent surveillance system for preventing the accidents are needed, such as Intelligent Surveillance Systems. in this paper, we propose a new crowd density estimation method which aims at estimating moving crowd using images from surveillance cameras situated in outdoor locations. The moving crowd is estimated from the area where using optical flow. The edge information is also used as feature to measure the crowd density, so we improve the accuracy of estimation of crowd density. A multilayer neural network is designed to classify crowd density into 5 classes. Finally the proposed method is experimented with PETS 2009 images.

• Proceedings of the Optical Society of Korea Conference
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• 2005.07a
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• pp.36-37
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• 2005

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2948-2963
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• 2015

In the field of computer vision, visual surveillance systems have recently become an important research topic. Growth in this area is being driven by both the increase in the availability of inexpensive computing devices and image sensors as well as the general inefficiency of manual surveillance and monitoring. In particular, the ultimate goal for many visual surveillance systems is to provide automatic activity recognition for events at a given site. A higher level of understanding of these activities requires certain lower-level computer vision tasks to be performed. So in this paper, we propose an intelligent activity recognition model that uses a structure learning method and a classification method. The structure learning method is provided as a K2-learning algorithm that generates Bayesian networks of causal relationships between sensors for a given activity. The statistical characteristics of the sensor values and the topological characteristics of the generated graphs are learned for each activity, and then a neural network is designed to classify the current activity according to the features extracted from the multiple sensor values that have been collected. Finally, the proposed method is implemented and tested by using PETS2013 benchmark data.