• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.129-133
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• 1994

Presented are closed-form expressions of the steady-state solution for the three-state exponentially correlated acceleration(ECA) target-tracking filter. The steady-state solution is derived based on Vaughan's approach for the case that the measurements of target position and velocity are available at discrete points in time. The solution for the ECA filter using only position measurements is obtained as a special case of the presented results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권11호
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• pp.5624-5638
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• 2016

A novel method based on Surf points is proposed to detect and lock-track single ground target in aerial videos. Videos captured by moving cameras contain complex motions, which bring difficulty in moving object detection. Our approach contains three parts: moving target template detection, search area estimation and target tracking. Global motion estimation and compensation are first made by grids-sampling Surf points selecting and matching. And then, the single ground target is detected by joint spatial-temporal information processing. The temporal process is made by calculating difference between compensated reference and current image and the spatial process is implementing morphological operations and adaptive binarization. The second part improves KALMAN filter with surf points scale information to predict target position and search area adaptively. Lastly, the local Surf points of target template are matched in this search region to realize target tracking. The long-term tracking is updated following target scaling, occlusion and large deformation. Experimental results show that the algorithm can correctly detect small moving target in dynamic scenes with complex motions. It is robust to vehicle dithering and target scale changing, rotation, especially partial occlusion or temporal complete occlusion. Comparing with traditional algorithms, our method enables real time operation, processing $520{\times}390$ frames at around 15fps.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.805-825
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• 2017

Correlation Filter-based Trackers (CFTs) gained popularity recently for their effectiveness and efficiency. To deal with the size changes of the target which may degenerate the tracking performance, scale estimation has been introduced in existing CFTs. However, the variations of the aspect ratio were usually neglected, which also influence the size of the target. In this paper, Size Aware Correlation Filter Trackers (SACFTs) are proposed to deal with this problem. The SACFTs not only determine the translation and scale variations, but also take the aspect ratio changes into consideration, thus a better estimation of the size of the target can be realized, which improves the overall tracking performance. And competing results can be achieved compared with state-of-the-art methods according to the experiments conducted on two large scale datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권2호
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• pp.710-726
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• 2018

This paper presents an extended particle filter to increase the accuracy and decrease the computation load of vehicle tracking. Particle filter has been the subject of extensive interest in video-based tracking which is capable of solving nonlinear and non-Gaussian problems. However, there still exist problems such as preventing unnecessary particle consumption, reducing the computational burden, and increasing the accuracy. We aim to increase the accuracy without an increase in computation load. In proposed method, we calculate the direction angle of the target vehicle. The angular difference between the direction of the target vehicle and each particle of the particle filter is observed. Particles are filtered and weighted, based on their angular difference. Particles with angular difference greater than a threshold is eliminated and the remaining are stored with greater weights in order to increase their probability for state estimation. Threshold value is very critical for performance. Thus, instead of having a constant threshold value, proposed algorithm updates it online. The first advantage of our algorithm is that it prevents the system from failures caused by insufficient amount of particles. Second advantage is to reduce the risk of using unnecessary number of particles in tracking which causes computation load. Proposed algorithm is compared against camshift, direction-based particle filter and condensation algorithms. Results show that the proposed algorithm outperforms the other methods in terms of accuracy, tracking duration and particle consumption.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.524-527
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• 1997

Conventional target tracking algorithms based on the linear estimation techniques perform quite efficiently when the target motion does not involve maneuvers. Target maneuvers involving short term accelerations, however, cause a bias in the measurement sequence. Accurate compensation for the bias requires processing more samples of which adds to the computational complexity. The primary motivation for employing a neural network for this task comes from the efficiency with which more features can be as inputs for bias compensation. A system architecture that efficiently integrates the fusion capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described. The parallel processing capability of a properly trained neural network can permit fast processing of features to yield correct acceleration estimates and hence can take the burden off the primary Kalman filter which still provides the target position and velocity estimates.

• 한국산학기술학회논문지
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• 제18권5호
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• pp.99-110
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• 2017

레이다를 이용한 다수 표적의 상태 추정을 통해 추적 성능을 향상시키는 문제는 중요하다. 클러터 환경에서 추적 필터를 이용하여 다수 표적 추적 시 트랙과 측정치 간의 결합사건이 발생하며 개수가 증가함에 따라 결합사건은 기하급수적으로 증가한다. 이러한 환경에서 다수 표적 추적 필터 설계 시 고려해야할 문제는 첫째, 신속한 거짓트랙 제거 및 표적트랙 확정을 통하여 오경보율 최소화하고, 이를 통해 FTD(False Track Discrimination) 성능을 높인다. 둘째, 다수의 트랙이 측정치를 공유하는 결합사건 발생시 효율적으로 각각의 측정치를 트랙에 할당함으로써 트랙 유지성능을 향상시키는 것이다. 두 가지 고려사항을 통해 단일 표적 추적 자료결합 기법을 다수 표적 추적 필터로 확장하여 사용하며, 대표적인 알고리듬으로 JIPDAF(Joint Integrated Probabilistic Data Association Filter)와 LM-IPDAF(Linear Multi-target IPDAF)가 있다. 본 논문에서는 측정치 할당 시 생기는 수 많은 가설들에 대한 확률적 평가를 하지 않음으로써 측정치와 트랙의 개수에 따라 비선형으로 연산량이 증가하지 않으며, 클러터밀도 추정을 통해 트랙을 쇄신하는 트랙존재확률 기반의 LM-IPDAF 알고리듬을 소개한다. 그리고 LM-IPDAF의 트랙존재확률 산출 시 필요한 클러터밀도 추정 방법을 개선함으로써 연산량을 효과적으로 감소시킬 수 있는 방법을 제안하고 시뮬레이션을 통해 기존의 알고리듬과 비교, 분석하여 성능을 검증하였다. 그 결과, 위치 RMSE, Confirmed True Track 측면에서는 동일한 성능을 내면서 시뮬레이션 처리 시간을 약 20% 감소시킬 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.329-334
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• 1998

This work is concerned with the problem of tracking a maneuvering target. In this paper, an error monitoring and recovery method of perception net is utilized to improve tracking performance for a highly maneuvering tar-get. Many researches have been performed in tracking a maneuvering target. The conventional Interacting Multiple Model (IMM) filter is well known as a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation scheme. The subfilters of IMM can be considered as fusing its initial value with new measurements. This approach is also shown in this paper. Perception net based error monitoring and recovery technique, which is a kind of geometric data fusion, makes it possible to monitor errors and to calibrate possible biases involved in sensed data and extracted features. Both detecting a maneuvering target and compensating the estimated state can be achieved by employing the properly implemented error monitoring and recovery technique. The IMM filter which employing the error monitoring and recovery technique shows good tracking performance for a highly maneuvering target as well as it reduces maximum values of estimation errors when maneuvering starts and finishes. The effectiveness of the pro-posed method is validated through simulation by comparing it with the conventional IMM algorithm.

• 한국항공우주학회지
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• 제39권11호
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• pp.1013-1020
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• 2011

본 논문에서는 이동 목표물을 추적하기 위하여, 다수 무인항공기의 측정치를 이용한 목표물의 위치와 속도 추정기법을 제안하였다. 항공기와 목표물 사이의 상대거리와 시선각 정보를 이용하여 목표물의 위치, 속도 성분을 추정하는 확장필터를 구성하였다. 다수의 항공기 간 정보교환과 계산이 용이하도록 공분산 역행렬 형태의 정보필터를 설계하였다. 개별 확장필터, 부분 분산형 확장필터, 분산형 확장필터를 이용한 수치 시뮬레이션을 수행하여, 제안된 분산형 확장필터의 이동 목표물 추정 및 추적 성능을 검증하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.649-652
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• 1999

In this paper, the design method of target tracking systems using the extended $H^{\infty}$ filter(EHF) is proposed. Usually, a Cartesian coordinate frame is tell suited to describe the target dynamics. However, the measurements made in radar-centered polar coordinates are expressed as nonlinear equations in Cartesian coordinates. Thus the tacking problem is concerned with the nonlinear estimation. The extended $H^{\infty}$ filter is able to deal with the problems arising in the target tacking systems such as the parameter uncertainty included inevitably in modeling physical systems mathematically, the unavailableness of the stochastic information about exogenous disturbances, and errors due to the linearization of measurement equations. We show the proposed filter is robuster than the extended Kalman filter(EKF) through a simple target tracking example.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.445-448
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• 1992

In this paper, we contructed extended Kalman filter for the image navigation systems. The conventional extended Kalman filter methode are simulated for nonlinear measurement systems. In addition, we designed a maneuvering target tracking filter using Singer's model technique and input estimation technique by Chan. Simulation results show that Chan's input estimation technique has performed better than Singer's technique.