• 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.

• Journal of Navigation and Port Research
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• v.26 no.5
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• pp.563-569
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• 2002

To overcome the weaknesses of conventional finite difference model in pollutant dispersion modelling, the particle tracking method is used. In this study, a three dimensional particle tracking model which can be used in Princeton Ocean Model was developed and verified through the various numerical tests. Usability of the model was also confirmed through the ocean outfall modelling in Tampa Bay, Florida. As it is expected, random walk model showed the less dispersion in a range compared to the conventional finite difference model and its reason is estimated due to an error from numerical diffusion which the conventional model holds. This newly developed model is expected to be used in various ocean dispersion modelling.

• Journal of Drive and Control
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• v.21 no.1
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• pp.8-15
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• 2024

This thesis investigates the jetting characteristics of an aerosol jet printing (AJP) process as a function of design and operating conditions. The governing equations of the AJP system are derived for experimentation and analysis. To understand the characteristics of the AJP system, it analyzes the jetting characteristics as a function of the flow rate of the carrier gas and the sheath gas, and the variation of the linewidth with the nozzle exit size based on particle tracking. The thesis focuses on computational fluid dynamics (CFD), which is a computer simulation. The particle tracking results obtained by CFD were analyzed using MATLAB. CFD analytical models can be analyzed in environments with different conditions and consider more specific situations than mathematical computational models. The validity of the CFD analysis is shown by comparing the experimental results with the CFD analysis.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.5
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• pp.766-778
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• 2015

In this paper, we design a hybrid system for recognition and tracking realized with the aid of polynomial based RBFNNs pattern classifier and particle filter. The RBFNN classifier is built by learning the training data for diverse pose images. The optimized parameters of RBFNN classifier are obtained by Particle Swarm Optimization(PSO). Testing data for pose image is used as a face image obtained under real situation, where the face image is detected by AdaBoost algorithm. In order to improve the recognition performance for a detected image, pose estimation as preprocessing step is carried out before the face recognition step. PCA is used for pose estimation, the pose of detected image is assigned for the built pose by considering the featured difference between the previously built pose image and the newly detected image. The recognition of detected image is performed through polynomial based RBFNN pattern classifier, and if the detected image is equal to target for tracking, the target will be traced by particle filter in real time. Moreover, when tracking is failed by PF, Adaboost algorithm detects facial area again, and the procedures of both the pose estimation and the image recognition are repeated as mentioned above. Finally, experimental results are compared and analyzed by using Honda/UCSD data known as benchmark DB.

• Journal of ILASS-Korea
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• v.10 no.3
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• pp.17-28
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• 2005

The Holographic Particle Velocimetry system can be a promising optical tool for the measurements of three dimensional particle velocities. In this study, diffused illumination holographic system to measure the sizes and 3D velocities of moving particles based on automatic image processing was developed. First of all basic optical systems for pulse laser recording, continuous laser reconstruction, and image acquisition, were constructed. To determine the position of particles in the optical axis, new three auto-focusing parameters(AEP), namely, Correlation Coefficient, Sharpness Index, and Depth Intensity were introduced and verified. The developed system was applied to spray droplets to validate the capability of the system. Three dimensional positions of particles viewed from two sides were decided using AFP and then 3D velocities of Particles were extracted by particle tracking algorithm. Comparison of measurement results of sizes and 3D velocities of particles with those obtained by laser instrument, PDPA, showed good consistency of the developed holographic system.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.4
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• pp.353-358
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• 2012

Human detection is a challenging task in many fields because it is difficult to detect humans due to their variable appearance and posture. Furthermore, it is also hard to track the detected human because of their dynamic and unpredictable behavior. The evaluation speed of method is also important as well as its accuracy. In this paper, we propose unified detection and tracking method for humans using Gaussian-PSO (Gaussian Particle Swarm Optimization) with the HOG (Histograms of Oriented Gradients) features to achieve a fast and accurate performance. Keeping the robustness of HOG features on human detection, we raise the process speed in detection and tracking so that it can be used for real-time applications. These advantages are given by a simple process which needs just one linear-SVM classifier with HOG features and Gaussian-PSO procedure for the both of detection and tracking.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.39-50
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• 2017

Moving object tracking techniques can be considered as one of the most essential technique in the video understanding of which the importance is much more emphasized recently. However, irregularity of light condition in the video, variations in shape and size of object, camera motion, and occlusion make it difficult to tracking moving object in the video. Swarm based methods are developed to improve the performance of Kalman filter and particle filter which are known as the most representative conventional methods, but these methods also need to consider dynamic property of moving object. This paper proposes adaptive parameter control method which can dynamically change weight value among parameters in particle swarm optimization. The proposed method classifies each particle to 3 groups, and assigns different weight values to improve object tracking performance. Experimental results show that our scheme shows considerable improvement of performance in tracking objects which have nonlinear movements such as occlusion or unexpected movement.

• 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.