• The KIPS Transactions:PartB
• /
• v.17B no.1
• /
• pp.37-46
• /
• 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 Society of Industry Convergence
• /
• v.27 no.2_2
• /
• pp.319-324
• /
• 2024

In this paper, the Artificial Intelligence Space(AI-Space) for human-robot interface is presented, which can enable human-computer interfacing, networked camera conferencing, industrial monitoring, service and training applications. We present a method for representing, tracking, and objects(human, robot, chair) following by fusing distributed multiple vision systems in AI-Space. The article presents the integration of color distributions into particle filtering. Particle filters provide a robust tracking framework under ambiguous conditions. We propose to track the moving objects(human, robot, chair) by generating hypotheses not in the image plane but on the top-view reconstruction of the scene.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.12 no.1
• /
• pp.60-65
• /
• 2012

This paper presents a method for occluded object based motion estimation and tracking system in dynamic image sequences using particle filter with 3D reconstruction. A unique characteristic of this study is its ability to cope with partial occlusion based continuous motion estimation using particle filter inspired from the mirror neuron system in human brain. To update a prior knowledge about the shape or motion of objects, firstly, fundamental 3D reconstruction based occlusion tracing method is applied and object landmarks are determined. And optical flow based motion vector is estimated from the movement of the landmarks. When arbitrary partial occlusions are occurred, the continuous motion of the hidden parts of object can be estimated by particle filter with optical flow. The resistance of the resulting estimation to partial occlusions enables the more accurate detection and handling of more severe occlusions.

• 한국가시화정보학회:학술대회논문집
• /
• 2006.12a
• /
• pp.113-116
• /
• 2006

AC-electroosmosis is one of the electrokinetic forces leading to phenomena peculiar in the microfluidics. This paper shows particle deformation in the microchannel with rectangular electrodes on the bottom wall for the AC-electroosmotic flows. We make a PDMS microchannnel with ITO electrodes To measure velocity distributions of the particles we used a three-dimensional particle tracking velocimetry (micro-PTV) technique this method is Particle tracking by interpolation the diffraction pattern ring diameter variations with the defocusing distances of base particle locations. we induce a function of frequency at the electrode. We find the velocity of particles is the most at the edge of the electrodes and Particles move to side wall or center of the channel for the bottom and middle.

• Journal of Korean Society for Atmospheric Environment
• /
• v.33 no.6
• /
• pp.583-592
• /
• 2017

A method is proposed for locating the heat sources from temperature observations, and its applicability is investigated for urban thermal environment simulations. A Lagrangian particle dispersion model, which is originally built for simulating the pollutants spread in the air, is exploited to identify the heat sources by transporting the Lagrangian heat particles backwards in time. The urban wind fields are estimated using a diagnostic meteorological model incorporating the morphological model for the urban canopy. The proposed method is tested for the horizontally homogeneous urban boundary layer problems. The effects of the turbulence levels and the computational time on the simulation are investigated.

• Smart Media Journal
• /
• v.4 no.1
• /
• pp.16-24
• /
• 2015

In this paper, we propose a method for multi-touch tracking using MRF-based particle filters and gesture likelihood estimation Each touch (of one finger) is considered to be one object. One of frequently occurring issues is the hijacking problem which means that an object tracker can be hijacked by neighboring object. If a predicted particle is close to an adjacent object then the particle's weight should be lowered by analysing the influence of neighboring objects for avoiding hijacking problem. We define a penalty function to lower the weights of those particles. MRF is a graph representation where a node is the location of a target object and an edge describes the adjacent relation of target object. It is easy to utilize MRF as data structure of adjacent objects. Moreover, since MRF graph representation is helpful to analyze multi-touch gestures, we describe how to define gesture likelihoods based on MRF. The experimental results show that the proposed method can avoid the occurrence of hijacking problems and is able to estimate gesture likelihoods with high accuracy.

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

• Smart Media Journal
• /
• v.2 no.4
• /
• pp.9-19
• /
• 2013

This paper gives a review on three illumination issues of face tracking on smart phones: dark scenes, sudden lighting change and backlit effect. First, we propose a fast and robust face tracking method utilizing continuous adaptive mean shift algorithm (CAMSHIFT) and CbCr skin locus. Initially, the skin locus obtained from training video data. After that, a modified CAMSHIFT version based on the skin locus is accordingly provided. Second, we suggest an enhancement method to increase the chance of detecting faces, an important initialization step for face tracking, under dark illumination. The proposed method works comparably with traditional CAMSHIFT or particle filter, and outperforms these methods when dealing with our public video data with the three illumination issues mentioned above.

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

• Industrial Engineering and Management Systems
• /
• v.16 no.1
• /
• pp.92-102
• /
• 2017

In this paper, we consider a typical health care system via the help of Wireless Sensor Network (WSN) for wireless patient tracking. The wireless patient tracking module of this system performs localization out of samples of Received Signal Strength (RSS) variations and tracking through a Particle Filter (PF) for WSN assisted by multiple transmit-power information. We propose a modified PF, Kullback-Leibler Distance (KLD)-resampling PF, to ameliorate the effect of RSS variations by generating a sample set near the high-likelihood region for improving the wireless patient tracking. The key idea of this method is to approximate a discrete distribution with an upper bound error on the KLD for reducing both location error and the number of particles used. To determine this bound error, an optimal algorithm is proposed based on the maximum gap error between the proposal and Sampling Important Resampling (SIR) algorithms. By setting up these values, a number of simulations using the health care system's data sets which contains the real RSSI measurements to evaluate the location error in term of various power levels and density nodes for all methods. Finally, we point out the effect of different power levels vs. different density nodes for the wireless patient tracking.