• Journal of Institute of Control, Robotics and Systems
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• v.18 no.3
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• pp.285-294
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• 2012

Using bearing measurement only, target motion state is not directly obtained so that TMA (Target Motion Analysis) is needed for this situation. TMA is a nonlinear estimation technique used in passive SONAR systems. Also it is the one of important techniques for underwater combat management systems. TMA can be divided to two parts: batch estimation and sequential estimation. It is preferable to use sequential estimation for reducing computational load as well as adaptively to target maneuvers, batch estimation is still required to attain target initial state vector for convergence of sequential estimation. Selection of batch time interval which depends on observability is critical in TMA performance. Batch estimation in general utilizes predetermined batch time interval. In this paper, we propose a new method called the BTIS (Batch Time Interval and Initial State Estimation). The proposed BTIS estimates target initial status and determines the batch time interval sequentially by using a bank of GMM-TS (Gaussian Mixture Measurement-Track Splitting) filters. The performance of the proposal method is verified by a Monte Carlo simulation study.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.2
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• pp.92-99
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• 2007

Welding fabrication invariantly involves three district sequential steps: preparation, actual process execution and post-weld inspection. One of the major problems in automating these steps and developing autonomous welding systems, is the lack of proper sensing strategies. Conventionally, machine vision is used in robotic arc welding only for the correction of pre-taught welding paths in single pass. In this paper, novel presented, developed vision processing techniques are detailed, and their application in welding fabrication is covered. The software for joint tracking system is finally proposed.

• Proceedings of the KSRS Conference
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• v.2
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• pp.946-949
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• 2006

With repeated coverage, spaceborne SAR (Synthetic Aperture Radar) instruments provide the most efficient means to monitor and study the changes in important elements of the marine environment. Due to highresolution of SAR data, the coverage of SAR sensor is always limited, especially for a repeat cycle. With more SAR sensors from various satellites, new data products such as ocean surface drift can be derived when two SARs' tracks overlap in a short time over coastal areas. Currently, there are two SAR sensors on different satellites with almost the exactly same path. That is, ERS-2 is following ENVISAT with a 30-minutes delay, which will be a good timing for ocean mesosclae feature tracking. For another application, a mystery ship near a big eddy with strong ship wake has been tracked between ERS-2 and ENVISAT SAR images to estimate its ship speed.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1203-1204
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• 2008

In this paper, a new stereo object tracking system using the disparity motion vector is proposed. In the proposed method, the time-sequential disparity motion vector can be estimated from the disparity vectors which are extracted from the sequence of the stereo input image pair and then using these disparity motion vectors, the area where the target object is located and its location coordinate are detected from the input stereo image. Basing on this location data of the target object, the pan/tilt embedded in the stereo camera system can be controlled and as a result, 3D tracking of the target object can be possible.

• Journal of Welding and Joining
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• v.16 no.3
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• pp.85-94
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• 1998

Welding fabrication invariantly involves three district sequential steps: preparation, actual process execution and post-weld inspection. One of the major problems in automating these steps and developing autonomous welding system, is the lack of proper sensing strategies. Conventionally, machine vision is used in robotic arc welding only for the correction of pre-taught welding paths in single pass. In this paper, developed vision processing techniques are detailed, and their application in welding fabrication is covered. The software for joint tracking system is finally proposed.

• The Journal of Korea Robotics Society
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• v.8 no.4
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• pp.266-272
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• 2013

Facial feature extraction and tracking are essential steps in human-robot-interaction (HRI) field such as face recognition, gaze estimation, and emotion recognition. Active shape model (ASM) is one of the successful generative models that extract the facial features. However, applying only ASM is not adequate for modeling a face in actual applications, because positions of facial features are unstably extracted due to limitation of the number of iterations in the ASM fitting algorithm. The unaccurate positions of facial features decrease the performance of the emotion recognition. In this paper, we propose real-time facial feature extraction and tracking framework using ASM and LK optical flow for emotion recognition. LK optical flow is desirable to estimate time-varying geometric parameters in sequential face images. In addition, we introduce a straightforward method to avoid tracking failure caused by partial occlusions that can be a serious problem for tracking based algorithm. Emotion recognition experiments with k-NN and SVM classifier shows over 95% classification accuracy for three emotions: "joy", "anger", and "disgust".

• Journal of Information Display
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• v.6 no.4
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• pp.16-25
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• 2005

In this paper, a new 3D object tracking system using the disparity motion vector (DMV) is presented. In the proposed method, the time-sequential disparity maps are extracted from the sequence of the stereo input image pairs and these disparity maps are used to sequentially estimate the DMV defined as a disparity difference between two consecutive disparity maps Similarly to motion vectors in the conventional video signals, the DMV provides us with motion information of a moving target by showing a relatively large change in the disparity values in the target areas. Accordingly, this DMV helps detect the target area and its location coordinates. Based on these location data of a moving target, the pan/tilt embedded in the stereo camera system can be controlled and consequently achieve real-time stereo tracking of a moving target. From the results of experiments with 9 frames of the stereo image pairs having 256x256 pixels, it is shown that the proposed DMV-based stereo object tracking system can track the moving target with a relatively low error ratio of about 3.05 % on average.

• 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 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.

• Journal of Astronomy and Space Sciences
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• v.36 no.4
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• pp.293-306
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• 2019

In this study, the observational arc-length effect on orbit determination (OD) for the Korea Pathfinder Lunar Orbiter (KPLO) in the Earth-Moon Transfer phase was investigated. For the OD, we employed a sequential estimation using the extended Kalman filter and a fixed-point smoother. The mission periods, comprised between the perigee maneuvers (PM) and the lunar orbit insertion (LOI) maneuver in a 3.5 phasing loop of the KPLO, was the primary target. The total period was divided into three phases: launch-PM1, PM1-PM3, and PM3-LOI. The Doppler and range data obtained from three tracking stations [included in the deep space network (DSN) and Korea Deep Space Antenna (KDSA)] were utilized for the OD. Six arc-length cases (24 hrs, 48 hrs, 60 hrs, 3 days, 4 days, and 5 days) were considered for the arc-length effect investigation. In order to evaluate the OD accuracy, we analyzed the position uncertainties, the precision of orbit overlaps, and the position differences between true and estimated trajectories. The maximum performance of 3-day OD approach was observed in the case of stable flight dynamics operations and robust navigation capability. This study provides a guideline for the flight dynamics operations of the KPLO in the trans-lunar phase.