• 한국항공우주학회지
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• 제49권11호
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• pp.953-960
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• 2021

거창 우주물체 레이저 추적 시스템은 인공위성 레이저 추적, 적응광학, 우주쓰레기 레이저 추적 등 과학 연구 및 국가적 미션을 수행하기 위해 100cm 크기의 광학 망원경을 가지고 있으며, 빠르고 정밀한 구동을 위하여 경위대식 방식의 추적마운트를 개발하여 테스트 관측 중에 있다. 최근 레이저 추적 시스템의 자동 관측 및 응용 연구 분야에서 추적 대상을 정밀하게 추적할 수 있는 능력은 시스템 성능을 판단할 수 있는 중요한 지표 중 하나이다. 그러기 위해서는 정밀 추적을 저해하는 요인을 파악하고 보정하기 위한 지향 보정 모델이 요구된다. 따라서 본 논문에서는 추적마운트 제작, 조립 및 설치 과정에서 발생 할 수 있는 정적 지향 오차 발생 원인들을 분석하였으며, 거창 우주물체 레이저 시스템의 추적마운트에 적용된 지향 보정 모델 및 최소자승법을 통해 추적마운트 파라미터를 추정하고, 지향 정밀도 분석 결과를 제시하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권6호
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• pp.3104-3120
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• 2017

The intelligent monitoring system has been successfully applied in many fields such as: monitoring of production lines, transportation, etc. Smart surveillance systems have been developed and proven effective in some specific areas such as monitoring of human activity, traffic, etc. Most of critical application monitoring systems involve object tracking as one of the key steps. However, task of tracking of moving object is not easy. In this paper, the authors propose a method to implement human object tracking in outdoor environment based on human features in shearlet domain. The proposed method uses shearlet transform which combines the human features with context-sensitiveness in order to improve the accuracy of human tracking. The proposed algorithm not only improves the edge accuracy, but also reduces wrong positions of the object between the frames. The authors validated the proposed method by calculating Euclidean distance and Mahalanobis distance values between centre of actual object and centre of tracked object, and it has been found that the proposed method gives better result than the other recent available methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권3호
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• pp.1121-1141
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• 2020

We propose a robust visual object tracking algorithm fusing a convolutional neural network tracker trained offline from a large number of video repositories and a color histogram based tracker to track objects for mixing immersive audio. Our algorithm addresses the problem of occlusion and large movements of the CNN based GOTURN generic object tracker. The key idea is the offline training of a binary classifier with the color histogram similarity values estimated via both trackers used in this method to opt appropriate tracker for target tracking and update both trackers with the predicted bounding box position of the target to continue tracking. Furthermore, a histogram similarity constraint is applied before updating the trackers to maximize the tracking accuracy. Finally, we compute the depth(z) of the target object by one of the prominent unsupervised monocular depth estimation algorithms to ensure the necessary 3D position of the tracked object to mix the immersive audio into that object. Our proposed algorithm demonstrates about 2% improved accuracy over the outperforming GOTURN algorithm in the existing VOT2014 tracking benchmark. Additionally, our tracker also works well to track multiple objects utilizing the concept of single object tracker but no demonstrations on any MOT benchmark.

• 대한임베디드공학회논문지
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• 제6권1호
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• pp.16-24
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• 2011

Automotive Radar Systems are currently under development for various applications to increase accuracy and reliability. The target tracking is most important in single or multiple target environments for accuracy. The tracking algorithm provides smoothed and predicted data for target position and velocity(Doppler). To this end, the fixed gain filter(${\alpha}{\beta}$ filter, ${\alpha}{\beta}{\gamma}$ filter) and dynamic filter(Kalman filter, Singer-Kalman filter, etc) are commonly used. Gating is used to decide whether an observation is assigned to an existing track or new track. Gating algorithms are normally based on computing a statistical error distance between an observation and prediction. The data association takes the observation-to-track pairings that satisfied gating and determines which observation-to-track assignment will actually be made. For data association, NNPDA(Nearest Neighbor Probabilistic Data Association) algorithm is proposed. In this paper, we designed a target tracking system developed for an Automotive Radar System. We show the experimental results of the 77GHz FMCW radar sensor on the roads. Four tracking algorithms(${\alpha}{\beta}$ filter, ${\alpha}{\beta}{\gamma}$ filter, 2nd order Kalman filter, Singer-Kalman filter) have been compared and analyzed to evaluate the performance in test scenario.

• 제어로봇시스템학회논문지
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• 제7권6호
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• pp.518-522
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• 2001

The main problem of the calibration of robots is to measure the position and orientation of a robot end effector. The calibration methods can be used as tool to improve the accuracy of robots without change of the arm or control architecture or robots. But such calibration methods require accurate measurements. Dynamic measurement of position and orientation provides a solution for this problem and improves dynamic accuracy by dynamic calibration of robots. This paper describes the development of the laser tracking system capable of determining the static and dynamic performance of industrial robots. The structure and systems components are presented and basic experimental results are included to demonstrated the instrument performance. The system can be applied to the remote controlled mobile robots as well s the calibration of robots.

• International Journal of Advanced Culture Technology
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• 제11권1호
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• pp.320-324
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• 2023

As urbanization accelerates and facilities that use energy increase, human life and property damage due to fire is increasing. Therefore, a fire monitoring system capable of quickly detecting a fire is required to reduce economic loss and human damage caused by a fire. In this study, we aim to develop an improved artificial intelligence model that can increase the accuracy of low fire alarms by mixing DeepSORT, which has strengths in object tracking, with the YOLOv5s model. In order to develop a fire detection model that is faster and more accurate than the existing artificial intelligence model, DeepSORT, a technology that complements and extends SORT as one of the most widely used frameworks for object tracking and YOLOv5s model, was selected and a mixed model was used and compared with the YOLOv5s model. As the final research result of this paper, the accuracy of YOLOv5s model was 96.3% and the number of frames per second was 30, and the YOLOv5s_DeepSORT mixed model was 0.9% higher in accuracy than YOLOv5s with an accuracy of 97.2% and number of frames per second: 30.

• 대한인간공학회지
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• 제31권4호
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• pp.601-607
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• 2012

Objective: The aim of this study is to classify between intentional and natural blinks in vision based eye tracking system. Through implementing the classification method, we expect that the great eye tracking method will be designed which will perform well both navigation and selection interactions. Background: Currently, eye tracking is widely used in order to increase immersion and interest of user by supporting natural user interface. Even though conventional eye tracking system is well focused on navigation interaction by tracking pupil movement, there is no breakthrough selection interaction method. Method: To determine classification threshold between intentional and natural blinks, we performed experiment by capturing eye images including intentional and natural blinks from 12 subjects. By analyzing successive eye images, two features such as eye closed duration and pupil size variation after eye open were collected. Then, the classification threshold was determined by performing SVM(Support Vector Machine) training. Results: Experimental results showed that the average detection accuracy of intentional blinks was 97.4% in wearable eye tracking system environments. Also, the detecting accuracy in non-wearable camera environment was 92.9% on the basis of the above used SVM classifier. Conclusion: By combining two features using SVM, we could implement the accurate selection interaction method in vision based eye tracking system. Application: The results of this research might help to improve efficiency and usability of vision based eye tracking method by supporting reliable selection interaction scheme.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 하계종합학술대회논문집
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• pp.635-638
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• 1998

This paper proposes a simple method to measure system's performance in target tracking problems. Essentially employing the Cramer-Rao lower bound (CRLB) on trakcing accuracy, an algorithm of predicting system's performance under various scenarios is developed. The input data is a collection of measurements over time fromsensors embedded in gaussian noise. The target of interest may not maneuver over the processing time interval while the own ship observing platform may maneuver in an arbitrary fashion. Th eproposed approach is demonstrated and discussed through simulation results.

• 전자공학회논문지S
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• 제34S권1호
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• pp.16-25
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• 1997

This paper shows the development of the high accuracy vehicle positioning algorithm using the differential technique in vehicle tracking systems form the existing vehicle position which is acquired from the global positioning system (GPS). The control center receives the satellite ephemerise data and pseudorange correction from the reference station, and vehicle position from the moving vehicle. The pseudorange is calculated with the satellite position and the vehicle position, and corrected by pseudorange correction. Using this corrected pseudorange and kalman filter, more improved vehicle positioning data were obtained.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.61.6-61
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• 2001

In this paper, we propose a method to analyze measured data from 3D Laser tracking system and to enhance precision performance of a Cartesian robot. Position data are obtained over the stroke of a Cartesian robot with variable speeds. The measured data is need to model errors with several different sources. In general, the error is a function of part accuracy, assembly accuracy, temperature, and control etc. After the sources of errors are identified, they are used to enhance precision performance. The proposed method is more complete than others because we use very accurate 3D Laser tracking system.