• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.1
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• pp.11-18
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• 2022

In order to improve the accuracy of particle tracking prediction techniques near the Korean Strait, this study compared and analyzed a particle tracking model based on a seawater flow numerical model and a machine learning based on a particle tracking model using field observation data. The data used in the study were the surface drifter buoy movement trajectory data observed in the Korea Strait, prediction data by machine learning (linear regression, decision tree) using the tide and wind data from three observation stations (Gageo Island, Geoje Island, Gyoboncho), and prediciton data by numerical models (ROMS, MOHID). The above three data were compared through three error evaluation methods (Correlation Coefficient (CC), Root Mean Square Errors (RMSE), and Normalized Cumulative Lagrangian Separation (NCLS)). As a final result, the decision tree model had the best prediction accuracy in CC and RMSE, and the MOHID model had the best prediction results in NCLS.

• 한국가시화정보학회:학술대회논문집
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• 2004.12a
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• pp.91-110
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• 2004

A comprehensive three-dimensional nano-particle tracking technique in micro- and nano-scale spatial resolution using the Total Internal Reflection Fluorescence Microscope (TIRFM) is discussed. Evanescent waves from the total internal reflection of a 488nm argon-ion laser are used to measure the hindered Brownian diffusion within few hundred nanometers of a glass-water interface. 200-nm fluorescence-coated polystyrene spheres are used as tracers to achieve three-dimensional tracking within the near-wall penetration depth. A novel ratiometric imaging technique coupled with a neural network model is used to tag and track the tracer particles. This technique allows for the determination of the relative depth wise locations of the particles. This analysis, to our knowledge is the first such three-dimensional ratiometric nano-particle tracking velocimetry technique to be applied for measuring Brownian diffusion close to the wall.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.693-709
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• 2018

In this paper, we propose a compact Convolutional Neural Network (CNN)-based tracker in conjunction with a particle filter architecture, in which the CNN model operates as an accurate candidates estimator, while the particle filter predicts the target motion dynamics, lowering the overall number of calculations and refines the resulting target bounding box. Experiments were conducted on the Online Object Tracking Benchmark (OTB) [34] dataset and comparison analysis in respect to other state-of-art has been performed based on accuracy and precision, indicating that the proposed algorithm outperforms all state-of-the-art trackers included in the OTB dataset, specifically, TLD [16], MIL [1], SCM [36] and ASLA [15]. Also, a comprehensive speed performance analysis showed average frames per second (FPS) among the top-10 trackers from the OTB dataset [34].

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.1
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• pp.35-42
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• 2012

In this study, a feature-points-tracking algorithm is suggested using a particle filter for vision based navigation system. By applying a dynamic model of the feature point, the tracking performance is increased in high dynamic condition, whereas a conventional KLT (Kanade-Lucas-Tomasi) cannot give a solution. Futhermore, the particle filter is introduced to cope with irregular characteristics of vision data. Post-processing of recorded vision data shows that the tracking performance of suggested algorithm is more robust than that of KLT in high dynamic condition.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.6
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• pp.954-959
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• 2022

Marine outfalls are used to discharge treated liquid effluents to the environment. An efficiently designed, constructed and operated marine outfall effectively dilutes the discharged effluent, thereby reducing the risk to biota and humans dependent upon the marine environment. In this study, we investigated the effluent transport from a marine outfall at different depths in Masan Bay. A particle-tracking model was used to predict the dispersion of effluent. The model results indicate that some particles released from a depth of 13 m move to the inner area of Masan Bay within 48 h. As the release depth increases after 48 h, the particles move further southward. This suggests that effluent from the outer area of Masan Bay can affect the inner area, and that this effect can be reduced by increasing the depth of effluent release.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.4
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• pp.211-222
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• 2020

This study suggested a method calculating the influence of effluent discharge from Saemangeum sluice-gates using the particle tracking model. For 2017, we presented the seasonal effects of effluent discharge as probability spatial distributions and compared with the results of the water age, one of the indicators of transport time scale. The influence of sluice-gates effluent discharge increases radially around Sinshi or Gaseok gates, which are expected to be biased toward the south in winter and north in summer due to the effect of seasonal winds. Although the results of the prediction are limited to the 2017 situation, the method of calculating the influence of sluice-gates effluent discharge using the Lagrangian particle tracking model can be used to predict the future of the around Saemangeum.

• 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.

• ALGAE
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• v.34 no.4
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• pp.315-326
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• 2019

Northeastward drifts of massive Sargassum patches were observed in the East China Sea (ECS) and Yellow Sea (YS) by the Geostationary Ocean Color Imager (GOCI) in May 2017. Coverage of the brown macroalgae patches was the largest ever recorded in the ECS and YS. Three-dimensional circulation modeling and Lagrangian particle tracking simulations were conducted to reproduce drifting trajectories of the macroalgae patches. The trajectories of the macroalgae patches were controlled by winds as well as surface currents. A windage (leeway) factor of 1% was chosen based on sensitivity simulations. Southerly winds in May 2017 contributed to farther northward intrusion of the brown macroalgae into the YS. Although satellite observation and numerical modeling have their own limitations and associated uncertainties, the two methods can be combined to find the best estimate of Sargassum patch trajectories. When satellites were unable to capture all patches because of clouds and sea fog in the ECS and YS, the Lagrangian particle tracking model helped to track and restore the missing patches in satellite images. This study suggests that satellite monitoring and numerical modeling are complementary to ensure accurate tracking of macroalgae patches in the ECS and YS.

• ETRI Journal
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• v.42 no.1
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• pp.54-66
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• 2020

Nowadays, the broad availability of cameras and embedded systems makes the application of computer vision very promising as a supporting technology for intelligent transportation systems, particularly in the field of vehicle tracking. Although there are several existing trackers, the limitation of using low-cost cameras, besides the relatively low processing power in embedded systems, makes most of these trackers useless. For the tracker to work under those conditions, the video frame rate must be reduced to decrease the burden on computation. However, doing this will make the vehicle seem to move faster on the observer's side. This phenomenon is called the fast motion challenge. This paper proposes a tracker called dynamic swarm particle (DSP), which solves the challenge. The term particle refers to the particle filter, while the term swarm refers to particle swarm optimization (PSO). The fundamental concept of our method is to exploit the continuity of vehicle dynamic motions by creating dynamic models based on PSO. Based on the experiments, DSP achieves a precision of 0.896 and success rate of 0.755. These results are better than those obtained by several other benchmark trackers.

• International Journal of Contents
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• v.10 no.3
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• pp.17-25
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• 2014

In this paper, we describe an adaptive Markov chain Monte Carlo-based particle filter that effectively addresses real-time multi-face tracking on mobile platforms. Because traditional approaches based on a particle filter require an enormous number of particles, the processing time is high. This is a serious issue, especially on low performance devices such as mobile phones. To resolve this problem, we developed a tracker that includes a more sophisticated likelihood model to reduce the number of particles and maintain the identity of the tracked faces. In our proposed tracker, the number of particles is adjusted during the sampling process using an adaptive sampling scheme. The adaptive sampling scheme is designed based on the average acceptance ratio of sampled particles of each face. Moreover, a likelihood model based on color information is combined with corner features to improve the accuracy of the sample measurement. The proposed tracker applied on various videos confirmed a significant decrease in processing time compared to traditional approaches.