• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2189-2196
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• 2016

This paper proposes a new finite control set-model predictive control (FCS-MPC) method for induction motors. In the method, the reference state that satisfies the given torque and rotor flux requirements is derived. Cost indices for the FCS-MPC are defined using the state tracking error, and a linear matrix inequality is formulated to obtain a proper weighting matrix for the state tracking error. The on-line procedure of the proposed FCS-MPC comprises of two steps: select the output voltage vector of the two level inverter minimizing the cost index and compute the optimal modulation factor of the minimizing output voltage vector in order to reduce the state tracking error and torque ripple. The steady state tracking error is removed by using an integrator to adjust the reference state. The simulation and experimental results demonstrated that the proposed FCS-MPC shows good torque, rotor flux control performances at different rotating speeds.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.305-326
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• 2019

Discriminative correlation filter (DCF) based tracking algorithms have recently shown impressive performance on benchmark datasets. However, amount of recent researches are vulnerable to heavy occlusions, irregular deformations and so on. In this paper, we intend to solve these problems and handle the contradiction between accuracy and real-time in the framework of tracking-by-detection. Firstly, we propose an innovative strategy to combine the template and color-based models instead of a simple linear superposition and rely on the strengths of both to promote the accuracy. Secondly, to enhance the discriminative power of the learned template model, the spatial regularization is introduced in the learning stage to penalize the objective boundary information corresponding to features in the background. Thirdly, we utilize a discriminative multi-scale estimate method to solve the problem of scale variations. Finally, we research strategies to limit the computational complexity of our tracker. Abundant experiments demonstrate that our tracker performs superiorly against several advanced algorithms on both the OTB2013 and OTB2015 datasets while maintaining the high frame rates.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.836-842
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• 2015

In this paper, the target size and model based target size estimator (TMBE) algorithm is presented for iimaging infrared (IIR) seeker. At the imaging seeker, target size information is important factor for accurate tracking. The model based target size estimator filter (MBEF) algorithm was proposed to estimate target size at imaging infrared seeker. But, the model based target size estimator filter algorithm need to know relative distance from the target. In order to overcome the problem, we propose target size and model based target size estimator filter (TMBEF) algorithm which based on the target size. The performance of proposed algorithm is tested at target intercept scenario. The experiment results show that the proposed algorithm has the accurate target size estimating performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.461-463
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• 2023

본 연구는 실시간 MOT(Multiple-Object-Tracking)의 성능을 향상시키기 위해 다양한 기법을 적용한 MOT 모델을 설계한다. 연구에서 사용하는 Backbone 모델은 TBD(Tracking-by-Detection) 기반의 Tracking 모델을 사용한다. Heatmap Detection을 통해 객체를 검출하고 Transformer 기반의 Feature를 연결하여 Tracking 한다. 제안하는 방법은 Anchor 기반의 Detection의 장시간 문제와 추적 객체 정보 전달손실을 감소하여 실시간 객체 추적에 도움이 될 것으로 사료된다.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.1
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• pp.96-107
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• 2014

Tracking the tactical object is a fundamental affair in network-equipped modern warfare. Geodetic coordinate system based on longitude, latitude, and height is suitable to represent the location of tactical objects considering multi platform data fusion. The motion of tactical object described as a dynamic model requires an appropriate filtering to overcome the system and measurement noise in acquiring information from multiple sensors. This paper introduces the filter suitable for multi-sensor data fusion and tactical object tracking, particularly the unscented transform(UT) and its detail. The UT in Unscented Kalman Filter(UKF) uses a few samples to estimate nonlinear-propagated statistic parameters, and UT has better performance and complexity than the conventional linearization method. We show the effects of UT-based filtering via simulation considering practical tactical object tracking scenario.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4929-4947
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• 2017

In visual object tracking, Correlation Filter-based Tracking (CFT) systems have arouse recently to be the most accurate and efficient methods. The CFT's circularly shifts the larger search window to find most likely position of the target. The need of larger search window to cover both background and object make an algorithm sensitive to the background and the target occlusions. Further, the use of fixed-sized windows for training makes them incapable to handle scale variations during tracking. To address these problems, we propose two layer target representation in which both global and local appearances of the target is considered. Multiple local patches in the local layer provide robustness to the background changes and the target occlusion. The target representation is enhanced by employing additional reversed RGB channels to prevent the loss of black objects in background during tracking. The final target position is obtained by the adaptive weighted average of confidence maps from global and local layers. Furthermore, the target scale variation in tracking is handled by the statistical model, which is governed by adaptive constraints to ensure reliability and accuracy in scale estimation. The proposed structural enhancement is tested on VTBv1.0 benchmark for its accuracy and robustness.

• International Journal of Control, Automation, and Systems
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• v.5 no.6
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• pp.630-642
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• 2007

In this paper, we propose a direct model reference adaptive fuzzy control (MRAFC) for MIMO nonlinear systems whose structure is represented by the Takagi-Sugeno fuzzy model. The adaptive law of the MRAFC estimates the approximation error of the fuzzy logic system so that it provides asymptotic tracking of the reference signal for the systems with uncertain or slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal. To verify the validity and effectiveness of the MRAFC scheme, the suggested analysis and design techniques are applied to the tracking control of robot manipulator and simulation studies are carried out. In the control design, the MRAFC is combined with feedforward PD control to make the actual joint trajectories of the robot manipulator with system uncertainties track the desired reference joint position trajectories asymptotically stably.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.187-192
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• 2008

The Intelligent Space(ISpace) provides challenging research fields for surveillance, human-computer interfacing, networked camera conferencing, industrial monitoring or service and training applications. ISpace is the space where many intelligent devices, such as computers and sensors, are distributed. According to the cooperation of many intelligent devices, the environment, it is very important that the system knows the location information to offer the useful services. In order to achieve these goals, we present a method for representing, tracking and human Jollowing by fusing distributed multiple vision systems in ISpace, with application to pedestrian tracking in a crowd. This paper describes appearance based unknown object tracking with the distributed vision system in intelligent space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.11C
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• pp.1071-1076
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• 2006

In this paper, we develop face and hand tracking for sign language recognition system. The system is divided into two stages; the initial and tracking stages. In initial stage, we use the skin feature to localize face and hands of signer. The ellipse model on CbCr space is constructed and used to detect skin color. After the skin regions have been segmented, face and hand blobs are defined by using size and facial feature with the assumption that the movement of face is less than that of hands in this signing scenario. In tracking stage, the motion estimation is applied only hand blobs, in which first and second derivative are used to compute the position of prediction of hands. We observed that there are errors in the value of tracking position between two consecutive frames in which velocity has changed abruptly. To improve the tracking performance, our proposed algorithm compensates the error of tracking position by using adaptive search area to re-compute the hand blobs. The experimental results indicate that our proposed method is able to decrease the prediction error up to 96.87% with negligible increase in computational complexity of up to 4%.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.226-233
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• 2022

Video surveillance is widely used in security surveillance, military navigation, intelligent transportation, etc. Its main research fields are pattern recognition, computer vision and artificial intelligence. This article uses OpenCV to detect and track vehicles, and monitors by establishing an adaptive model on a stationary background. Compared with traditional vehicle detection, it not only has the advantages of low price, convenient installation and maintenance, and wide monitoring range, but also can be used on the road. The intelligent analysis and processing of the scene image using CAMSHIFT tracking algorithm can collect all kinds of traffic flow parameters (including the number of vehicles in a period of time) and the specific position of vehicles at the same time, so as to solve the vehicle offset. It is reliable in operation and has high practical value.