• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.5
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• pp.161-172
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• 2018

This paper proposed the robot vision control method to track a moving rigid body target using the vision system model that can actively control camera parameters even if the relative position between the camera and the robot and the focal length and posture of the camera change. The proposed robotic vision control scheme uses a batch method that uses all the vision data acquired from each moving point of the robot. To process all acquired data, this robot vision control scheme is divided into two cases. One is to give an equal weight for all acquired data, the other is to give weighting for the recent data acquired near the target. Finally, using the two proposed robot vision control schemes, experiments were performed to estimate the positions of a moving rigid body target whose spatial positions are unknown but only the vision data values are known. The efficiency of each control scheme is evaluated by comparing the accuracy through the experimental results of each control scheme.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.637-638
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• 2012

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.1017-1028
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• 2019

Pedestrian tracking is a particular object tracking problem and an important component in various vision-based applications, such as autonomous cars and surveillance systems. Following several years of development, pedestrian tracking in videos remains challenging, owing to the diversity of object appearances and surrounding environments. In this research, we proposed a tracking-by-detection system for pedestrian tracking, which incorporates a convolutional neural network (CNN) and color information. Pedestrians in video frames are localized using a CNN-based algorithm, and then detected pedestrians are assigned to their corresponding tracklets based on similarities between color distributions. The experimental results show that our system is able to overcome various difficulties to produce highly accurate tracking results.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.239-240
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• 2012

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.10
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• pp.620-629
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• 2005

In this paper, a new protocol-based approach was proposed for development of a ubiquitous vision system. It is possible to apply the approach by regarding the ubiquitous vision system as a multiagent system. Thus, each vision sensor can be regarded as an agent (vision agent). Each vision agent independently performs exact segmentation for a target by color and motion information, visual tracking for multiple targets in real-time, and location estimation by a simple perspective transform. Matching problem for the identity of a target during handover between vision agents is solved by the Identified Contract Net (ICN) protocol implemented for the protocol-based approach. The protocol-based approach by the ICN protocol is independent of the number of vision agents and moreover the approach doesn't need calibration and overlapped region between vision agents. Therefore, the ICN protocol raises speed, scalability, and modularity of the system. The protocol-based approach was successfully applied for our ubiquitous vision system and operated well through several experiments.

• Electronics and Telecommunications Trends
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• v.37 no.1
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• pp.73-83
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• 2022

Visual object tracking can be utilized in various applications and has attracted considerable attention in the field of computer vision. Visual object tracking technology is classified in various ways based on the number of tracking objects and the methodologies employed for tracking algorithms. This report briefly introduces the visual object tracking challenge that contributes to the development of single object tracking technology. Furthermore, we review ten Siamese network-based algorithms that have attracted attention, owing to their high tracking speed (despite the use of neural networks). In addition, we discuss the prospects of the Siamese network-based object tracking algorithms.

• Journal of the Ergonomics Society of Korea
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• v.19 no.1
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• pp.91-100
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• 2000

In this paper the intent recognition system which recognizes the human's head movements as a simple non-verbal intent representation is presented. The system recognizes five basic intent representations. i.e., strong/weak affirmation. strong/weak negation, and ambiguity by image processing of nodding or shaking movements of head. The vision system for tracking the head movements is composed of CCD camera, image processing board and personal computer. The modified template matching method which replaces the reference image with the searched target image in the previous step is used for the robust tracking of the head movements. For the improvement of the processing speed, the searching is performed in the pyramid representation of the original image. By inspecting the variance of the head movement trajectories. we can recognizes the two basic intent representations - affirmation and negation. Also, by focusing the speed of the head movements, we can see the possibility which recognizes the strength of the intent representation.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.123-125
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• 2006

This paper discusses the method of vision based sensor fusion system for biped robot walking. Most researches on biped walking robot have mostly focused on walking algorithm itself. However, developing vision systems for biped walking robot is an important and urgent issue since biped walking robots are ultimately developed not only for researches but to be utilized in real life. In the research, systems for environment recognition and tole-operation have been developed for task assignment and execution of biped robot as well as for human robot interaction (HRI) system. For carrying out certain tasks, an object tracking system using modified optical flow algorithm and obstacle recognition system using enhanced template matching and hierarchical support vector machine algorithm by wireless vision camera are implemented with sensor fusion system using other sensors installed in a biped walking robot. Also systems for robot manipulating and communication with user have been developed for robot.

• Special Issue of the Society of Naval Architects of Korea
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• 2013.12a
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• pp.73-80
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• 2013

From broad point of view, seam tracking has been one of main issues with respect to welding automation. Several attempts have been successful for seam tracking of fixed weaving width. As a solution of the seam tracking methods for varying groove width, the visual sensors such as CCD cameras have been adopted. Although the vision sensing techniques can achieve high accuracy, the weak point is that well-prepared vision sensor environment should be required to obtain high-quality visual measurements which can be easily affected by significant noises in industrial areas. This paper proposed an alternative seam tracking algorithm for narrow groove. A special measurement device for arc voltage, in this study, is developed to enhance the reliability of the measured welding signals. Based on the developed arc sensor algorithm, an automatic weld-width tracking algorithm is also proposed, which is able to predict the weld-position more accurately. The usefulness of the automatic weld-width tracking algorithm was well verified by applying it to gas tungsten arc welding (GTAW).

• Journal of Korea Multimedia Society
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• v.24 no.6
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• pp.796-804
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• 2021

In computer vision, visual tracking method addresses the problem of localizing an specific object in video sequence according to the bounding box. In this paper, we propose a tracking method by introducing the feature correlation comparison into the siamese network to increase its matching identification. We propose a way to compute location of object to improve matching performance by a correlation operation, which locates parts for solving the searching problem. The higher layer in the network can extract a lot of object information. The lower layer has many location information. To reduce error rate of the object center point, we built a siamese network that extracts the distribution and location information of target objects. As a result of the experiment, the average center error rate was less than 25%.