• The Journal of Korea Robotics Society
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• v.2 no.4
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• pp.310-316
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• 2007

The paper proposes a human-following behavior of mobile robot and an intelligent space (ISpace) is used in order to achieve these goals. An ISpace is a 3-D environment in which many sensors and intelligent devices are distributed. Mobile robots exist in this space as physical agents providing humans with services. A mobile robot is controlled to track a walking human using distributed intelligent sensors as stably and precisely as possible. The moving objects is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the intelligent space. Uncertainties in the position estimation caused by the point-object assumption are compensated using the Kalman filter. To generate the shortest time trajectory to track the walking human, the linear and angular velocities are estimated and utilized. The computer simulation and experimental results of estimating and trackinging of the walking human with the mobile robot are presented.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.259-262
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• 1997

This paper presents a visual servoing of RV-M2 robot manipulators to track and grasp moving object, using pruned dynamic recurrent neural networks(DRNN). The object is stationary in the robot work space and the robot is tracking and grasping the object by using CCD camera mounted on the end-effector. In order to optimize the structure of DRNN, we decide the node whether delete or add, by mutation probability, first in case of delete node, the node which have minimum sum of input weight is actually deleted, and then in case of add node, the weight is connected according to the number of case which added node can reach the other nodes. Using evolutionary programming(EP) that search the struture and weight of the DRNN, and evolution strategies(ES) which train the weight of neuron, we pruned the net structure of DRNN. We applied the DRNN to the Visual Servoing of a robot manipulators to control position and orientation of end-effector, and the validity and effectiveness of the pro osed control scheme will be verified by computer simulations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.293-297
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• 2007

This thesis suggests a new algorithm to detects multiple moving objects using a CMODE(Correct Multiple Object DEtection) method in the color images acquired in real-time and to track the interested pedestrian using motion and hue information. The multiple objects are detected, and then shaking trees or moving cars are removed using structural characteristics and shape information of the man , the interested pedestrian can be detected, The first similarity judgment for tracking an interested pedestrian is to use the distance between the previous interested pedestrian's centroid and the present pedestrian's centroid. For the area where the first similarity is detected, three feature points are calculated using k-mean algorithm, and the second similarity is judged and tracked using the average hue value for the $3{\times}3$ area of each feature point. The zooming of camera is adjusted to track an interested pedestrian at a long distance easily and the FOV(Field of View) of camera is adjusted in case the pedestrian is not situated in the fixed range of the screen. As a experiment results, comparing the suggested CMODE method with the labeling method, an average approach rate is one fourth of labeling method, and an average detecting time is faster three times than labeling method. Even in a complex background, such as the areas where trees are shaking or cars are moving, or the area of shadows, interested pedestrian detection is showed a high detection rate of average 96.5%. The tracking of an interested pedestrian is showed high tracking rate of average 95% using the information of situation and hue, and interested pedestrian can be tracked successively through a camera FOV and zooming adjustment.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.1
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• pp.89-101
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• 2015

This paper proposes a statistical model of 3-D positions when tracking moving targets using the uncooled infrared (IR) stereo camera system. The proposed model is derived from two errors. One is the position error which is caused by the sampling pixels in the digital image. The other is the timing jitter which results from the irregular capture-timing in the infrared cameras. The capture-timing in the IR camera is measured using the jitter meter designed in this paper, and the observed jitters are statistically modeled as Gaussian distribution. This paper derives an integrated probability distribution by combining jitter error with pixel position error. The combined error is modeled as the convolution of two error distributions. To verify the proposed statistical position error model, this paper has some experiments in tracking moving objects with IR stereo camera. The 3-D positions of object are accurately measured by the trajectory scanner, and 3-D positions are also estimated by stereo matching from IR stereo camera system. According to the experiments, the positions of moving object are estimated within the statistically reliable range which is derived by convolution of two probability models of pixel position error and timing jitter respectively. It is expected that the proposed statistical model can be applied to estimate the uncertain 3-D positions of moving objects in the diverse fields.

• Journal of Digital Convergence
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• v.13 no.12
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• pp.209-215
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• 2015

In the past, there have been many theory and algorithms for vehicle tracking. But the time complexity of many feature point matching methods for vehicle tracking are exponential. Also, object segmentation and detection algorithms presented for vehicle tracking are exhaustive and time consuming. Therefore, we present the fast and efficient two stages method that can efficiently track the many moving vehicles on the road. The first detects the vehicle plate regions and extracts the feature points of vehicle plates. The second associates the feature points between frames using dynamic programming.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1987.04a
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• pp.60-66
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• 1987

In this paper a fast matching method using subtemplate and search and down technique to reduce very heavy computational load of the conventional matching method, is presented The proposed method is spplied to an automatic target tracker in order to track one moving object in comparatively simple backgoriund. Experimental results show that istperformanced is not so degraded in spite of high computational reduction as that of the conventional matching method.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.168.1-168
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• 2001

This article describes a novel model-based compensating neural network (NN) model developed to be used in our active binocular head controller, which addresses both the kinematics and dynamics aspects in trying to precisely track a moving object of interest to keep it in view. The compensating NN model is constructed using two classes of self-tuning neural models: namely Neural Gas (NG) algorithm and SoftMax function networks. The resultant servo controller is shown to be able to handle the tracking problem with a minimum knowledge of the dynamic aspects of the system.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.1
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• pp.63-71
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• 1988

In this paper, a fast matching method using hierarchical neighborhood search and subtemplate to reduce very heavy computational load of the conventional matching method, is presented. Some parameters of the proposed method are chosen so that an automatic target tracker to which it is applied can track one moving object well in comparatively simple background. Experimental results show that its performance is not so degraded in spite of high computational reduction over that of the matching method using 3-step search.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2100-2103
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• 2003

기존의 객체 추출 및 추적 기법은 외형 변화가 없는 객체를 대상으로 하거나 배경이 고정된 영상만을 고려하였다 본 논문에서는 영역의 색상과 움직임 정보, 그리고 인접한 영역의 상관 관계를 고려한 Markov Random Field (MRF) 모델을 제안한다. MRF 모델은 영상의 시간적 공간적 상관성을 기반으로 최적의 레이블 셋을 계산함으로써 보다 정확하게 객체를 추출 및 추적할 수 있다. 또한, 블록 기반 움직임 추출 알고리즘인 Diamond Search (DS)를 분할된 영역에 적용하여 빠르게 영역의 움직임과 전역 움직임을 추정하였다. 실험 결과 제안한 방법이 객체의 외형 변화와 카메라 움직임이 있는 동영상에서 빠른 속도로 정확하게 객체를 추출 및 추적하는 것을 확인하였다.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.245-248
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• 2001

본 논문은 히스토그램 백 프로젝션, 히스토그램 인터 섹션 그리고 XY-프로젝션을 이용하여 물체를 분할하고 정합하여 물체 추적 시스템에 적용하고자 한다. 물체 추적 시스템에서 실시간 처리를 위하여 물체정합 모델은 계산량이 적고, 물체의 변화에도 일관성이 있어야 한다. 본 논문에서 제안한 물체정합 모델은 이러한 물체 추적 시스템에 적합하다. 본 논문에서는 움직이는 카메라로부터 획득된 영상에서 물체를 정합하는 것을 보였으며, 물체를 큰 오차 없이 추적함을 보였다.