• Journal of Mechanical Science and Technology
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• v.18 no.7
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• pp.1236-1245
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• 2004

In this study, an optical imaging method was developed for the measurements of the sizes and velocities of droplets in sprays. Double-exposure single-frame spray images were captured by the imaging system. An image processing program was developed for the measurements of the sizes and positions of individual particles including separation of the overlapped particles and particle tracking and pairing at two time instants. To recognize and separate overlapping particles, the morphological method based on watershed segmentation as well as separation using the perimeter and convex hull of image was used consecutively. Better results in separation were obtained by utilization of both methods especially for the multiple or heavily-overlapped particles. The match probability method was adopted for particle tracking and pairing after identifying the positions of individual particles and it produced good matching results even for large particles like droplets in sprays. Therefore, the developed optical imaging method could provide a reliable way of analyzing the motion and size distribution of droplets produced by various sprays and atomization devices.

• The KSFM Journal of Fluid Machinery
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• v.17 no.6
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• pp.44-51
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• 2014

The disinfection method using UV has emerged as photodissociation in water disinfection. In order to predict performance for UV disinfection, CFD analysis was performed due to saving cost. Most CFD studies of UV reactor have used particle tracking method. However it demands additional analysis time, computing resource and phase besides working fluid. In this paper, pathogenic microorganisms' route is assumed to streamline of fluid to save computing time. the computational results are in good agreement with experimental results. The results of streamline method are compared with the particle tracking method. In conclusion, the effectiveness of streamline method for UV disinfection are confirmed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1826-1835
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• 2007

In this paper, we present a new approach for automatic detection and tracking for multiple targets. We combine a highest probability data association(HPDA) algorithm for target detection with a particle filter for multiple target tracking. The proposed approach evaluates the probabilities of one-to-one assignments of measurement-to-track and the measurement with the highest probability is selected to be target- originated, and the measurement is used for probabilistic weight update of particle filtering. The performance of the proposed algorithm for target tracking in clutter is compared with the existing clustering algorithm and the sequential monte carlo method for probability hypothesis density(SMC PHD) algorithm for multi-target detection and tracking. Computer simulation studies demonstrate that the HPDA algorithm is robust in performing automatic detection and tracking for multiple targets even though the environment is hostile in terms of high clutter density and low target detection probability.

• The Journal of Korea Robotics Society
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• v.8 no.2
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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.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.8
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• pp.883-887
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• 2010

3D face tracking is one of essential techniques in computer vision such as surveillance, HCI (Human-Computer Interface), Entertainment and etc. However, 3D face tracking demands high computational cost. It is a serious obstacle to applying 3D face tracking to mobile devices which usually have low computing capacity. In this paper, to reduce computational cost of 3D tracking and extend 3D face tracking to mobile devices, an efficient particle filtering method using MLESAC(Maximum Likelihood Estimation SAmple Consensus) motion estimation is proposed. Finally, its speed and performance are evaluated experimentally.

• Journal of Korea Water Resources Association
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• v.37 no.4
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• pp.293-304
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• 2004

Sedimentation basin plays an important role in urban water treatment, and there are many complicated phenomena which need to be understood for efficient design and control of it. Especially, the study on the improvement of settling efficiency is required. In this study, commercial CFD (Computational Fluid Dynamics) program, FLUENT, and particle tracking method were used to simulate the flow in sedimentation basin, and to predict the settling efficiency. Computational domain of real scale was made, and detail factors such as porous wall, and outlet trough were considered instead of being simplified. The simulation results were compared with the experimental data to calibrate the parameters of particle tracking method. Sensitivity analysis showed that the particle diameter had more significant effects on settling efficiency than the particle density. The computation results gave the best agreements with the experimental data, when the value of particle diameter was 26.5 ${\mu}{\textrm}{m}$.

• 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 the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.139-147
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• 2014

In this paper, we present a novel method for simultaneous detection and tracking of multiple objects using dual-layer particle filtering. The proposed dual-layer particle sampling (DLPS) algorithm consists of parent-particles (PP) in the first layer for detecting multiple objects and child-particles (CP) in the second layer for tracking objects. In the first layer, PPs detect persons using a classifier trained by the intersection kernel support vector machine (IKSVM) at each particle under a randomly selected scale. If a certain PP detects a person, it generates CPs, and makes an object model in the detected object region for tracking the detected object. While PPs that have detected objects generate CPs for tracking, the rest of PPs still move for detecting objects. Experimental results show that the proposed method can automatically detect and track multiple objects, and efficiently reduce the processing time using the sampled particles based on motion distribution in video sequences.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.102-109
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• 2014

A particle system is used for modeling the physical phenomenon. There are many traditional ways for simulation modeling which can be well suited for application including the landscapes of branches, clouds, waves, fog, rain, snow and fireworks in the three-dimensional space. In this paper, we present a new fireworks modeling technique for modeling 3D firework based on Firework Particle Tracking (FPT) using the particle system. Our method can track and recognize the launched and exploded particle of fireworks, and extracts relatively accurate 3D positions of the particles using 3D depth values. It can realize 3D simulation by using tracking information such as position, speed, color and life time of the firework particle. We exploit Region of Interest (ROI) for fast particle extraction and the prevention of false particle extraction caused by noise. Moreover, Kalman filter is used to enhance the robustness in launch step. We propose a new fireworks particle tracking method for the efficient tracking of particles by considering maximum moving range and moving direction of particles, and shall show that the 3D speeds of particles can be obtained by finding the rotation angles of fireworks. Also, we carry out the performance evaluation of particle tracking: tracking speed and accuracy for tracking, classification, rotation angle respectively with respect to four types of fireworks: sphere, circle, chrysanthemum and heart.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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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.