• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.451-465
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• 2017

This paper presents a novel discriminative visual tracking algorithm with an adaptive incremental extreme learning machine. The parameters for an adaptive incremental extreme learning machine are initialized at the first frame with a target that is manually assigned. At each frame, the training samples are collected and random Haar-like features are extracted. The proposed tracker updates the overall output weights for each frame, and the updated tracker is used to estimate the new location of the target in the next frame. The adaptive learning rate for the update of the overall output weights is estimated by using the confidence of the predicted target location at the current frame. Our experimental results indicate that the proposed tracker can manage various difficulties and can achieve better performance than other state-of-the-art trackers.

• International Journal of Aeronautical and Space Sciences
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• v.2 no.2
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• pp.73-81
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• 2001

This paper describes the procedure to develop a robust estimator design method for a target tracker that accounts for both structured real parameter uncertainties and unknown inputs. Two robust design approaches are combined: the Mini-p-Norm. design method to consider real parameter uncertainties and the $H_{\infty}$ design technique for unknown disturbances and unknown inputs. Constant estimator gains are computed that guarantee the robust performance of the estimator in the presence of parameter variations in the target model and unknown inputs to the target. The new estimator has two design parameters. One design parameter allows the trade off between small estimator error variance and low sensitivity to unknown parameter variations. Another design parameter allows the trade off between the robustness to real parameter variations and the robustness to unknown inputs. This robust estimator design method was applied to the longitudinal motion tracking problem of a T-38 aircraft.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.468-473
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• 1998

An intelligent multimode target tracking algorithm using fuzzy logic is presented. Multimode tracking represents a synergistic approach that utilizes a variety of tracking techniques(centroid, correlation, etc.) to overcome the limitations inherent in any single-mode tracker. The design challenge for this type of multimode tracker is the data fusion algorithm. designs for this algorithm are based on heuristic rather than analytical approaches. A correlation-tracking algorithm seeks to align the incoming target image with a reference in age of the target, but has a critical problem, so called drift phenomenon. In this paper we will suggest a robust correlation tracker with gradient preprocessor combined by centroid algorithm to overcome the drift problem.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.335-337
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• 1994

The correlation tracker developed by John M. Fitts in 1979 is the most complex to mechanize but provides the best tracking performance in a low SNR condition. Correlation tracker would rewove the requirements for optimizing threshold and has no need to know information about the target. But if the displacement of the target is large, the tracking error of the correlation tracker tends to diverge. In this paper, we suggest a precision image tracking algorithm which improves the tracking performance via iterative application of the matched filter estimation algorithm.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.947-952
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• 2021

In this paper, the simplified kinematic error model for Delta parallel robot is presented, which can enable the analytical forward kinematics essentially for kinematic calibration calculations instead of the numerical one. The simplified kinematic error model is proposed and the forward kinematics including the error parameters is analytically derived. The kinematic calibration algorithm of the Delta parallel robot with 90 degree arrangement using laser tracker and the experiment result are presented.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.444-448
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• 2008

The ubiquitous smart home is the home of the future that takes advantage of context information from the human and the home environment and provides an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. We present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. We used four network cameras for real-time human tracking. This paper explains the real-time human tracker's architecture, and presents an algorithm with the details of two functions (prediction of human location and motion) in the real-time human tracker. The human location uses three kinds of background images (IMAGE1: empty room image, IMAGE2:image with furniture and home appliances in the home, IMAGE3: image with IMAGE2 and the human). The real-time human tracker decides whether the human is included with which furniture (or home appliance) through an analysis of three images, and predicts human motion using a support vector machine. A performance experiment of the human's location, which uses three images, took an average of 0.037 seconds. The SVM's feature of human's motion recognition is decided from pixel number by array line of the moving object. We evaluated each motion 1000 times. The average accuracy of all the motions was found to be 86.5%.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.395-396
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• 2010

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1095-1098
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• 1996

In this work, attitude determination with Inertial Reference Unit as attitude sensor is considered. Usually, the attitude error from IRU increases because of gyro rate bias and noise. Therefore, other attitude sensors(sun sensor, horizon sensor, star tracker) are needed to compensate for error from IRU. In this paper, we use the extended Kalman filter for attitude estimation of spacecraft with IRU and star tracker.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1986.10a
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• pp.186-190
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• 1986

This paper describes a way of implementing the segmentation of 128*128 pixel images to be used as the inputs. to a real-time automatic video tracker. The suggested method uses the lowest valley-value of the computed intensity historgram with 16 levels. This method improves smoothing effects and also significantly reduces hardware requirements. Entire segmentation process is caried out in 10msec thus making a real time application possible.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.485-488
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• 2003

A conventional correlation target tracker is analysed with a simple mathematical approach. And, we will propose a correlation measure with selective attentional property in order to overcome the false-peak problem of the conventional methods. Various experimental results show that the proposed correlation measure is able to reduce considerably the probability of false-peaks degraded by the correlation between background images of a reference block and a distorted and noisy sensor input image.