• 대한임베디드공학회논문지
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• 제17권6호
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• pp.355-365
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• 2022

This paper addresses the problem of real-time tracking a high-speed ballistic target. Particle filters can be considered to overcome the nonlinearity in motion and measurement models in the ballistic target. However, it is difficult to apply particle filters to real-time systems because particle filters generally require much computation time. This paper proposes an accelerated particle filter using graphics processing unit (GPU) for real-time ballistic target tracking. The real-time performance of the proposed method was tested and analyzed on a widely-used embedded system. The comparison results with the conventional particle filter on CPU (central processing unit) showed that the proposed method improved the real-time performance by reducing computation time significantly.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권2호
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• pp.118-123
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• 2011

Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.

• 대한전자공학회논문지SP
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• 제45권5호
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• pp.63-71
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• 2008

본 논문에서는 다관절체 추적을 위해 기존에 물체 추적에 자주 이용되는 파티클 필터를 확장한 계층적 파티클 필터 방법을 제안한다. 칼라 특징은 부분 겹침, 회전등에 강건한 특징을 가지고 있어서, 칼라 기반 파티클 필터는 물체 추적에 널리 쓰이고 있다. 다관절체 추적에서 상태 벡터는 높은 차원을 가지기 때문에 기존의 파티클 필터를 이용해 바람직한 추적 결과를 얻기 위해서는 많은 수의 샘플이 요구된다. 이러한 문제점을 해결하기 위해, 본 논문에서는 이미 알고 있는 다른 신체 부위의 위치를 이용해 계층적으로 신체 부위를 추적한다. 계층적 추적 방법에 의해 복잡한 환경에서 강건한 추적을 위한 샘플의 수를 줄일 수 있었다. 또한 포즈를 인식하기 위해 상박과 하박의 각도를 이용한 SVM(Support Vector Machine)을 이용해 8개의 포즈를 분류한다. 실험 결과는 세안한 방법이 기존의 칼라 기반의 파티클 필터보다 효율적임을 보여준다.

• Ocean Science Journal
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• 제40권4호
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• pp.201-208
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• 2005

An accurate particle tracking method for a finite difference method model is developed using a constant acceleration method. Being assumed constant temporal and spatial gradients, the new method permits temporal-spatial variability of particle velocity. Test results in a solid rotating flow show that the new method has second-order accuracy. The performance of the new method is compared with that of other methods; the first-order Euler forward method, and the second-order Euler predictor-corrector method. The new method is the most efficient method among the three. It is more accurate and efficient than the other two.

• ETRI Journal
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• 제38권2호
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• pp.356-365
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• 2016

This research proposes a modified particle filter to increase the accuracy of vehicle tracking in a noisy and occluded medium. In our proposed method for vehicle tracking, the direction angle of a target vehicle is calculated. The angular difference between the motion direction of the target vehicle and each particle of the particle filter is observed. Particles are filtered and weighted depending on their angular distance to the motion direction. Those particles moving in a direction similar to that of the target vehicle are assigned larger weights; this, in turn, increases their probability in a given likelihood function (part of the process of estimation of a target's state parameters). The proposed method is compared against a condensation algorithm. Our results show that the proposed method improves the stability of a particle filter tracker and decreases the particle consumption.

• 한국컴퓨터정보학회논문지
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• 제22권1호
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• pp.1-7
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• 2017

In this paper, we propose an efficient method that is tracking an object in real time using particle filter and adaptive observation model. When tracking object, it happens object shape variation by camera or object movement in variety environments. The traditional method has an error of tracking from these variation, because it has fixed observation model about the selected object by the user in the initial frame. In order to overcome these problems, we propose a method that updates the observation model by calculating the similarity between the used observation model and the eight-way of edge model from the current position. If the similarity is higher than the threshold value, tracking the object using updated observation model to reset observation model. On the contrary to this, the algorithm which consists of a process is to maintain the used observation model. Finally, this paper demonstrates the performance of the stable tracking through comparison with the traditional method by using a number of experimental data.

• IEIE Transactions on Smart Processing and Computing
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• 제1권2호
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• pp.78-87
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• 2012

In this paper, we address the tracking problem caused by camera motion and rolling shutter effects associated with CMOS sensors in consumer handheld cameras, such as mobile cameras, digital cameras, and digital camcorders. A modified particle filtering method is proposed for simultaneously tracking objects and compensating for the effects of camera motion. The proposed method uses an elastic registration algorithm (ER) that considers the global affine motion as well as the brightness and contrast between images, assuming that camera motion results in an affine transform of the image between two successive frames. By assuming that the camera motion is modeled globally by an affine transform, only the global affine model instead of the local model was considered. Only the brightness parameter was used in intensity variation. The contrast parameters used in the original ER algorithm were ignored because the change in illumination is small enough between temporally adjacent frames. The proposed particle filtering consists of the following four steps: (i) prediction step, (ii) compensating prediction state error based on camera motion estimation, (iii) update step and (iv) re-sampling step. A larger number of particles are needed when camera motion generates a prediction state error of an object at the prediction step. The proposed method robustly tracks the object of interest by compensating for the prediction state error using the affine motion model estimated from ER. Experimental results show that the proposed method outperforms the conventional particle filter, and can track moving objects robustly in consumer handheld imaging devices.

• 한국군사과학기술학회지
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• 제14권3호
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• pp.517-523
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• 2011

The estimation filter in radar systems must track targets' position within low tracking error. In the Multi-Function Radar(MFR), ${\alpha}-{\beta}$ filter and Kalman filter are widely used to track single or multiple targets. However, due to target maneuvering, these filters may not reduce tracking error, therefore, may lost target tracks. In this paper, a target tracking filter based on particle filtering algorithm is proposed for the MFR. The advantage of this method is that it can track targets within low tracking error while targets maneuver and reduce impoverishment of particles by the proposed resampling method. From the simulation results, the improved tracking performance is obtained by the proposed filtering algorithm.

• 한국전자통신학회논문지
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• 제9권2호
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• pp.143-148
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• 2014

파티클 필터는 비선형 비가우시안 추정 문제에 매우 효과적인 수단으로 비디오 영상에서 객체를 추적하는 경우에 널리 이용되어왔다. 하지만 객체의 이동이 심한 경우 객체의 추적을 위해서는 매우 많은 개수의 파티클이 있어야 하므로 계산량이 크게 증가하게 된다. 본 논문에서는 프레임간의 객체 이동이 상당히 크게 이루어지는 low frame rate(LPR) 비디오에서 차량의 추적을 위하여 모션 벡터를 이용한 개선된 파티클 필터 추적 방법을 제안하고 실험을 통하여 성능을 평가하였다. 제안한 파티클 필터에서는 selection 단계와 observe 단계의 두 단계에서 모션 벡터를 적용하였다. 실험 결과 제안한 방법은 LPR 영상에서 기존의 파티클 필터가 객체의 추적에 실패하는 경우에도 성공적 추적이 가능하며, 추적의 정확도 또한 향상되었음을 보여주었다.