• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.16-22
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• 2011

This paper presents the multi-object tracking approach using the background difference and particle filtering by monte carlo sampling. We apply particle filters based on probabilistic importance sampling to multi-object independently. We formulate the object observation model by the histogram distribution using color information and the object dynaminc model for the object motion information. Our approach does not increase computational complexity and derive stable performance. We implement the whole Bayesian maximum likelihood framework and describes robust methods coping with the real-world object tracking situation by the observation and transition model.

• Nuclear Engineering and Technology
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• v.42 no.2
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• pp.211-218
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• 2010

An analysis model on debris transport in the containment floor of pressurized water reactors is developed in which the flow field is calculated by Eulerian conservation equations of mass and momentum and the debris particles are traced by Lagrange equations of motion using the pre-determined flow field data. For the flow field calculation, two-dimensional Shallow Water Equations derived from Navier Stokes equations are solved using the Finite Volume Method, and the Harten-Lax-van Leer scheme is used for accuracy to capture the dry-to-wet interface. For the debris tracing, a simplified two-dimensional Lagrangian particle tracking model including drag force is developed. Advanced schemes to find the positions of particles over the containment floor and to determine the position of particles reflected from the solid wall are implemented. The present model is applied to calculate the transport fraction to the Hold-up Volume Tank in Advanced Power Reactors 1400. By the present model, the debris transport fraction is predicted, and the effect of particle density and particle size on transport is investigated.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.3
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• pp.831-840
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• 2016

Hydrodynamic condition can be used to predict particle movement within water column and the results used to optimize environmental conditions for effective site selection, setting of environmental quality standard, waste dispersion, and pathogen transfer. To predict the extent of movement of particle from land, 3D hydrodynamic model that includes particle tracking module was applied to Geoje Bay and to calibrate particle tracking model, floating buoy measurement is operated. The model results show that short time is required for particles released into system from river to be transported to the shellfish farming area. It takes about 2 days for the particles to shellfish farming area under mean flow condition. It meant Geoje Bay, especially shellfish farming area is vulnerable to anthropogenic waste from river.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1524-1529
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• 2005

Particle filter is a kind of conditional density propagation model. Its similar characteristics to both selection and mutation operator of evolutionary strategy (ES) due to its Bayesian inference rule structure, shows better performance than any other tracking algorithms. When a new object is entering the region of interest, particle filter sets which have been swarming around the existing objects have to move and track the new one instantaneously. Moreover, there is another problem that it could not track multiple objects well if they were moving away from each other after having been overlapped. To resolve reinitialization problem, we use competitive-AVQ algorithm of neural network. And we regard interfarme difference (IFD) of background images as potential field and give priority to the particles according to this IFD to track multiple objects independently. In this paper, we showed that the possibility of real-time object tracking as intelligent interfaces by simulating the deformable contour particle filters.

• Ocean Science Journal
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• v.40 no.4
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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.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.37-46
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• 2010

In this paper we have presented a two dimensional model based tracking system using improved chamfer matching. Conventional chamfer matching could not calculate similarity well between the object and image when there is very cluttered background. Then we have improved chamfer matching to calculate similarity well even in very cluttered background with edge and corner feature points. Improved chamfer matching is used as likelihood function of particle filter which tracks the geometric object. Geometric model which uses edge and corner feature points, is a discriminant descriptor in color changes. Particle Filter is more non-linear tracking system than Kalman Filter. Then the presented method uses geometric model, particle filter and improved chamfer matching for tracking object in complex environment. In experimental result, the robustness of our system is proved by comparing other methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.586-591
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• 2009

In this paper, we propose the mobile object tracking algorithm based on the feature vector using particle filter. To do this, first, we detect the movement area of mobile object by using RGB color model and extract the feature vectors of the input image by using the KLT-algorithm. And then, we get the first feature vectors by matching extracted feature vectors to the detected movement area. Second, we detect new movement area of the mobile objects by using RGB and HSI color model, and get the new feature vectors by applying the new feature vectors to the snake algorithm. And then, we find the second feature vectors by applying the second feature vectors to new movement area. So, we design the mobile object tracking algorithm by applying the second feature vectors to particle filter. Finally, we validate the applicability of the proposed method through the experience in a complex environment.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.467-473
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• 2007

In this paper, we present a position-based robot hand tracking scheme where a pan-tilt camera is controlled such that a robot hand is always shown in the center of an image frame. We calculate the rotation angles of a pan-tilt camera by transforming the coordinate systems. In order to identify the model parameters, we applied two optimization techniques: a nonlinear least square optimizer and a particle swarm optimizer. From the simulation results, it is shown that the considered parameter identification problem is characterized by a highly multimodal landscape; thus, a global optimization technique such as a particle swarm optimization could be a promising tool to identify the model parameters of a robot hand tracking system, whereas the nonlinear least square optimizer often failed to find an optimal solution even when the initial candidate solutions were selected close to the true optimum.

• Journal of Ocean Engineering and Technology
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• v.23 no.2
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• pp.47-52
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• 2009

A Lagrangian particle tracking model coupled with the Princeton Ocean Model were used to estimate the average residence time of coastal water in Masan Bay, Korea. Our interest in quantifying the transport time scales in Masan Bay was stimulated by the search for a mechanistic understanding of this spatial variability, which is consistent with the concept of spatially variable transport time scales. Tidal simulation was calibrated through a comparison with the results of semi-diurnal current and water elevation measured at the tidal stations of Masan, Gadeokdo. In the model simulations, particles were released in eight cases, including slack before ebb, peak ebb, slack before flood, and peak flood, during both spring and neap tides. The averaged values obtained from the particle release simulations were used for the average residence times of the coastal water in Masan Bay. The average residence times for the southeastern parts of Somodo and the Samho River, Masan Bay were estimated to be about 20~50days and 70~80days, respectively. The spatial difference for the average residence time was controlled by the tidal currents and distance from the mouth of the bay. Our results might provide useful for understanding the transport and behavior of coastal water in a bay and might be used to estimate the dissimilative capacity for environmental assessment.

• Journal of Internet Computing and Services
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• v.17 no.3
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• pp.25-32
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• 2016

In this paper, we propose a method that is tracking an object in real time using particle filter and the observation model with edge. First of all, the proposed method defines the object to be tracked in the initial frame. Then, it generates the edge observation model for the object to be tracked and a set of particles. It calculates the weight by comparing the average of the middle distance in eight-way of particle filter edge model with that in edge observation model, and then updates the weight with the calculated value. After resampling particles using the updated weights, it estimates the current location of the tracked object. Finally, this paper demonstrates the performance of the stable tracking through comparison with the existing method by using a number of experimental data.