• Journal of Soil and Groundwater Environment
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• v.13 no.6
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• pp.50-61
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• 2008

In this study, new integrated particle tracking algorithm was developed to reduce the inherent problem of Eulerian- Lagrangian method, or adverse effect of particle tracking error on mass balance error. The new integrated particle tracking algorithm includes numerical method, semi-analytical method, and analytical method which consider both temporal and spatial changes of velocity field during time step. Detail of mathematical derivations is well illustrated and four examples are made to verify through the comparison of the new integrated particle tracking with analytical solution or Runge-Kutta method. Additionally, It was shown that the there is better superiority of the new integrated particle tracking algorithm over other existing particle tracking method such as Lu's method.

• Annual Conference of KIPS
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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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.315-324
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• 2023

In this paper, we propose a real-time pose estimation method for a monocular camera using a particle filter integrated with UKF (unscented Kalman filter). While conventional camera tracking techniques combine camera images with data from additional devices such as gyroscopes and accelerometers, the proposed method aims to use only two-dimensional visual information from the camera without additional sensors. This leads to a significant simplification in the hardware configuration. The proposed approach is based on a particle filter integrated with UKF. The pose of the camera is estimated using UKF, which is defined individually for each particle. Statistics regarding the camera state are derived from all particles of the particle filter, from which the real-time camera pose information is computed. The proposed method demonstrates robust tracking, even in the case of rapid camera shakes and severe scene occlusions. The experiments show that our method remains robust even when most of the feature points in the image are obscured. In addition, we verify that when the number of particles is 35, the processing time per frame is approximately 25ms, which confirms that there are no issues with real-time processing.

• International Journal of Contents
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• v.11 no.3
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• pp.47-53
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• 2015

Visual object tracking is a fundamental problem in the field of computer vision, as it needs a proper model to account for drastic appearance changes that are caused by shape, textural, and illumination variations. In this paper, we propose a feature-based visual-object-tracking method with a sparse representation. Generally, most appearance-based models use the gray-scale pixel values of the input image, but this might be insufficient for a description of the target object under a variety of conditions. To obtain the proper information regarding the target object, the following combination of features has been exploited as a corresponding representation: First, the features of the target templates are extracted by using the HOG (histogram of gradient) and LBPs (local binary patterns); secondly, a feature-based sparsity is attained by solving the minimization problems, whereby the target object is represented by the selection of the minimum reconstruction error. The strengths of both features are exploited to enhance the overall performance of the tracker; furthermore, the proposed method is integrated with the particle-filter framework and achieves a promising result in terms of challenging tracking videos.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.408-414
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• 2009

A GPS/INS system is widely used in the UGV to estimate position during the mission. However, there are few restrictions when a GPS/INS system used alone. For example, GPS provides precise location information but easily interrupted by external factors like weather, environment, etc. INS provides continuous location data but positioning errors grew very rapidly with time. Therefore, it is necessary to integrating multi-sensors for more continuous and correct position estimation. In this paper, we propose location estimation algorithm of the UGV for GPS/INS integrated system that combines Fuzzy Inference and Unscented Particle Filter(UPF) to improve navigation. Fuzzy inference provides the simplest method integrating GPS/INS and UPF is non-linear estimation filter well suited to the correction of errors. The performance of the proposed algorithm was tested to compare with other algorithms. the results show that the proposed algorithm is more accuracy in position estimation and ensures continuous position tracking.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.2
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• pp.201-211
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• 2022

At the center of the Noryang waterway, the Gwangyang bay area (including the Yeosu Strait) is located at the west, and the Jinju bay area (including Gangjin bay and Sacheon bay) is located at the east. Freshwater from several rivers is flowing into the study area. In particula,r the event of flood, great quantities freshwater flow from Seomjingang (Seomjin river) into the Gwangyang bay area and from Gahwacheon (discharge from Namgang Dam) into the Jinju bay. The Gwangyang and Jinju bay are connected to the Noryang waterway. In addition, freshwater from Seomjingang and Gahwacheon also affect through the Noryang waterway. In this study, we elucidated the characteristics of the tidal exchange rate and residence time for dry season and flood season on 50 frequency, considering freshwater from 51 rivers, including Seomjingang and Gahwacheon, using a particle tracking method. We conducted additional experiments to determine the effect of freshwater from Seomjingang and Gahwacheon during flooding. In both the dry season and flood season, the result showed that the particles released from the Gwangyang bay moved to the Jinju bay through the Noryang waterway. However, comparatively small amount of particles moved from the Jinju bay to the Gwangyang bay. Each experimental case, the sea exchange rate was 44.40~67.21% in the Gwangyang bay and 50.37~73.10% in the Jinju bay, and the average residence time was 7.07~15.36days in the Gwangyang bay and 6.45~12.75days in the Jinju bay. Consequently the sea exchange rate increased and the residence time decreased during flooding. A calculation of cross-section water flux over 30 days for 7 internal and 5 external areas, indicated that the main essential flow direction of the water flux was the river outflow water from Seomjingang flow through the Yeosu strait to the outer sea and from Gahwacheon flow through Sacheon bay, Jinju bay and the Daebang waterway to the outer sea.