• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2328-2344
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• 2022

2D human pose estimation still faces difficulty in low-resolution images. Most existing top-down approaches scale up the target human bonding box images to the large size and insert the scaled image into the network. Due to up-sampling, artifacts occur in the low-resolution target images, and the degraded images adversely affect the accurate estimation of the joint positions. To address this issue, we propose a multi-resolution input feature fusion network for human pose estimation. Specifically, the bounding box image of the target human is rescaled to multiple input images of various sizes, and the features extracted from the multiple images are fused in the network. Moreover, we introduce a guiding channel which induces the multi-resolution input features to alternatively affect the network according to the resolution of the target image. We conduct experiments on MS COCO dataset which is a representative dataset for 2D human pose estimation, where our method achieves superior performance compared to the strong baseline HRNet and the previous state-of-the-art methods.

• Journal of Satellite, Information and Communications
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• v.11 no.4
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• pp.81-88
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• 2016

In this paper, we propose an adaptive multi-target estimation algorithm using the characteristics of signals in the IR-UWB(Impulse-Radio Ultra Wideband) radar system, which is attracting attention because it has good transparency, robustness to the indoor environment, and high precision positioning of tens of centimeters. We proposed an algorithm that estimates multiple peaks with the characteristic that the signal reflected by the target has a peak. To verify the performance of these algorithms, multiple targets were placed in front of the radar and the existing technique and the multi - target estimation algorithm were compared. The location of the targets is estimated in real time with one transmitting antenna and one receiving antenna. The number of estimates can be increased compared with the existing peak signal derivation method, and multiple targets can be derived. The conventional technique estimates only one target, which results in a mean square error of 1 while a multi - target estimation algorithm yields a result of about 0.05. The proposed method is expected to be able to apply multiple targets to the estimation and application in one IR-UWB module environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.10
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• pp.1874-1881
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• 2010

A recursive linear stochastic error compensation algorithm is newly proposed for target localization in sensor network which provides range difference of arrival(RDOA) measurements. Target localization with RDOA is a well-known nonlinear estimation problem. Since it can not solve with a closed-form solution, the numerical methods sensitive to initial guess are often used before. As an alternative solution, a pseudo-linear estimation scheme has been used but the auto-correlation of measurement noise still causes unacceptable estimation errors under low SNR conditions. To overcome these problems, a stochastic error compensation method is applied for the target localization problem under the assumption that a priori stochastic information of RDOA measurement noise is available. Apart from the existing methods, the proposed linear target localization scheme can recursively compute the target position estimate which converges to true position in probability. In addition, it is remarked that the suggested algorithm has a structural reconciliation with the existing one such as linear correction least squares(LCLS) estimator. Through the computer simulations, it is demonstrated that the proposed method shows better performance than the LCLS method and guarantees fast and reliable convergence characteristic compared to the nonlinear method.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.6
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• pp.403-410
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• 2016

This paper proposes a robust image-based visual servoing scheme using a nonlinear observer for a monocular eye-in-hand manipulator. The proposed control method is divided into a range estimation phase and a target-tracking phase. In the range estimation phase, the range from the camera to the target is estimated under the non-moving target condition to solve the uncertainty of an interaction matrix. Then, in the target-tracking phase, the feature point uncertainty caused by the unknown motion of the target is estimated and feature point errors converge sufficiently near to zero through compensation for the feature point uncertainty.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1205-1211
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• 2008

This paper describes methods to estimate 3-D position of target with respect to reference frame through monocular image from unmanned aerial vehicle (UAV). 3-D position of target is used as information for surveillance, recognition and attack. In this paper. 3-D position of target is estimated to make guidance and control law, which can follow target, user interested. It is necessary that position of target is measured in image to solve 3-D position of target. In this paper, kalman filter is used to track and output position of target in image. Estimation of target's 3-D position is possible using result of image tracking and information of UAV and camera. To estimate this, two algorithms are used. One is methode from arithmetic derivation of dynamics between UAV, carmer, and target. The other is LPV (Linear Parametric Varying). These methods have been run on simulation, and compared in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.5095-5111
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• 2016

The Probability Hypothesis Density (PHD) filter is a suboptimal approximation and tractable alternative to the multi-target Bayesian filter based on random finite sets. However, the PHD filter fails to track newborn targets when the target birth intensity is unknown prior to tracking. In this paper, a dual detection-guided newborn target intensity PHD algorithm is developed to solve the problem, where two schemes, namely, a newborn target intensity estimation scheme and improved measurement-driven scheme, are proposed. First, the newborn target intensity estimation scheme, consisting of the Dirichlet distribution with the negative exponent parameter and target velocity feature, is used to recursively estimate the target birth intensity. Then, an improved measurement-driven scheme is introduced to reduce the errors of the estimated number of targets and computational load. Simulation results demonstrate that the proposed algorithm can achieve good performance in terms of target states, target number and computational load when the newborn target intensity is not predefined in multi-target tracking systems.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.8
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• pp.842-848
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• 2014

In this paper, an improved method RMBE (Resizable Model Based target size Estimator) is presented for SDIIR (Strap Down Imaging Infrared) seekers. At the target engaging scenario, the IIR target measurement is separated by various parts. In this case, target object changing is important to accurate target intercept. Therefore, we need robust target size estimator. Our proposed method resize estimated target size with MC-1 (Markov Chain I) for accurate target size estimation. The performance of proposed method is tested at IIR target tracking of target intercept scenario. The experiment results show that the proposed RMBE has improved performance than MBE.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.632-637
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• 2008

In real system application, the propeller shaft rate (PSR) estimation algorithm for the feature extraction of the sonar target operates with the following problems: it requires both accurate and efficient the fundamental finding method because it is essential and difficult to distinguish harmonic family composed of the fundamental and its harmonics from the multiple spectral lines in the frequency spectrum-based sonar target classification, and further, it requires an easy design procedure in terms of its structures and parameters. To solve these problems, an evolutionary PSR estimation algorithm using an expert knowledge and the evolution strategy, is proposed. To verify the performance of the proposed algorithm, a sonar target PSR estimation is performed. Simulation results show that the proposed algorithm effectively solves the problems in the realtime system application.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.147-152
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• 2012

This paper estimates the direction of arrival of desired a target using propagation wave in spatial. Direction of arrival estimation is to find desired target position among received signal to receiver array antennas. In this paper, we estimated direction of arrival for target, by using cost function and high resolution MUSIC algorithm, in order to direction of arrival estimation, and calculated optimum weight vector. Through simulation, in regard to the estimation of the arrival direction of a target, the performances of the existing ESPRIT algorithm and the proposed algorithm were comparatively analyzed. In the estimation time of the arrival direction of a target object, the proposed algorithm showed an improvement of approximately as compared to the existing ESPRIT algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.530-535
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• 2016

In this paper, the TTSE (Target size and Triangulation-based target Size Estimator) algorithm is proposed to estimate static target size in an imaging environment. The target size information is an important factor for accurate imaging target tracking. However, the imaging sensor cannot generate distance between the missile and target to calculate the target size. To overcome the problem, we propose the TTSE algorithm, which is based on target size and triangulation. The proposed method performance is tested in a target intercept scenario. The experiment results show that the proposed algorithm has better performance than the conventional algorithm (ET-TSE) for accurate CCD target size estimation.