• ETRI Journal
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• 제45권3호
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• pp.394-403
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• 2023

Direct tracking problem of moving noncircular sources for multiple arrays is investigated in this study. Here, we propose an improved unscented particle filter (I-UPF) direct tracking method, which combines system proportional symmetry unscented particle filter and Markov Chain Monte Carlo (MCMC) algorithm. Noncircular sources can extend the dimension of sources matrix, and the direct tracking accuracy is improved. This method uses multiple arrays to receive sources. Firstly, set up a direct tracking model through consecutive time and Doppler information. Subsequently, based on the improved unscented particle filter algorithm, the proposed tracking model is to improve the direct tracking accuracy and reduce computational complexity. Simulation results show that the proposed improved unscented particle filter algorithm for noncircular sources has enhanced tracking accuracy than Markov Chain Monte Carlo unscented particle filter algorithm, Markov Chain Monte Carlo extended Kalman particle filter, and two-step tracking method.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2012년도 추계학술발표대회
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• pp.552-555
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• 2012

The main goal of this paper is to develop a robust visual tracking algorithm with particle filtering. Visual Tracking with particle filter technique is not easy task due to cluttered environment, illumination changes. To deal with these problems, we develop an efficient observation model for target tracking with particle filter. We develop a robust phase correlation combined with motion information based observation model for particle filter framework. Phase correlation provides straight-forward estimation of rigid translational motion between two images, which is based on the well-known Fourier shift property. Phase correlation has the advantage that it is not affected by any intensity or contrast differences between two images. On the other hand, motion cue is also very well known technique and widely used due to its simplicity. Therefore, we apply the phase correlation integrated with motion information in particle filter framework for robust tracking. In experimental results, we show that tracking with multiple cues based model provides more reliable performance than single cue.

• 수산해양교육연구
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• 제28권4호
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• pp.1143-1151
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• 2016

To study the dispersion process and residence time of anthropogenic pollutant in Masan bay, a three-dimensional hydrodynamic model coupled to a particle tracking model, EFDC, is applied. Particle tracking model simulated the instantaneous release of particles emulating discharge from river and wastewater treatment plant to show the behaviour of pollutant in terms of water circulation and water exchange. Modelled outcomes for water circulation were in good agreement with tidal elevation and current data. The results of particle tracking model show that over half of particles released from northern Masan bay transport to out of area while the particles from Dukdong wastewater treatment plant transport to northern area. This meant pollution source from inside and outside of the northern area can affect water quality of northern Masan bay.

• 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.

• 한국항행학회논문지
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• 제15권4호
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• pp.556-561
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• 2011

일반적으로 비선형 시스템에서 particle filter가 Kalman Filter보다 표적추적 성능이 뛰어나다고 알려져 있다. 그러나 particle filter는 많은 연산량을 요구하는 단점이 있다. 본 논문에서는 particle filter 보다 적은 particle의 수, 즉 적은 연산량으로 동일한 성능을 가지는 Rao-Blackwellized particle filter의 모델의 민감성을 줄인 Rao-Blackwellized Multiple Model Particle Filter(RBMMPF)의 알고리즘을 소개하고 이에 다중센서 정보를 융합하는 자료융합 기법을 적용하였다. 시뮬레이션을 통해 단일센서 정보를 이용한 RBMMPF 표적추적 성능과 다중센서정보를 융합한 RBMMPF의 표적추적 성능을 비교, 분석하였다.

• 한국항해항만학회지
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• 제26권5호
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• pp.563-569
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• 2002

오염물질 확산모델링시 기존의 유한차분모델의 단점을 보완하기 위해 입자추적법이 사용되고 있다. 본 연구에서는 Princeton Ocean Model에 결합하여 사용할 수 있는 3차원 입자추적모델을 개발하였으며 이를 다양한 수치실험을 통해 검증하였다. 또한 미국 플로리다 주 템파만의 해양방류구 모델링에 적용하므로써 모델의 유용성을 확인하였다. 예상대로 입자추적모델은 기존의 유한차분모델에 비해 적은 확산범위를 나타내었으며, 이는 기존의 유한차분모델이 안고 있는 수치확산에 따른 오차로 추정된다. 새로이 개발된 모델은 다양한 해양확산모델링에 유용하게 응용될 것으로 기대된다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2016년도 춘계학술발표대회
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• pp.743-744
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• 2016

Object tracking in video sequences is a challenging task and has various applications. Particle filtering has been proven very successful for non-Gaussian and non-linear estimation problems. In this study, we first try to develop a color-based particle filter. In this approach, the color distributions of video frames are integrated into particle filtering. Color distributions are applied because of their robustness and computational efficiency. The model of the particle filter is defined by the color information of the tracked object. The model is compared with the current hypotheses of the particle filter using the Bhattacharyya coefficient. The proposed tracking method directly incorporates the scale and motion changes of the objects. Experimental results have been presented to show the effectiveness of our proposed system.

• 한국환경과학회지
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• 제6권3호
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• pp.239-247
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• 1997

The numerical experiments using a particle tracking model have been performed for predicting the change of water Quality and shoreline. In present study, comparison of the numerical model results with the analytic solution shows that the point of the mainmum concentration and the distribution pattern is very similar. The reflection effect from the boundary was newly Introduced for making clear the effect of the closed boundary which set limits to application of a particle tracking model. The present model seems to reappear physical phenomenon well. This model shows well qualitative appearance of pollutant diffusion in Kwangan beach. Therefore, this model is regarded as a useful means for predicting diffusion movement of suspended sand, and change of water quality.

• 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.

• 로봇학회논문지
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• 제8권2호
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• pp.104-115
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• 2013

For natural human-robot interaction, we need to know location and shape of facial feature in real environment. In order to track facial feature robustly, we can use the method combining particle filter and active appearance model. However, processing speed of this method is too slow. In this paper, we propose two ideas to improve efficiency of this method. The first idea is changing the number of particles situationally. And the second idea is switching the prediction model situationally. Experimental results is presented to show that the proposed method is about three times faster than the method combining particle filter and active appearance model, whereas the performance of the proposed method is maintained.