• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1139-1145
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• 2014

User recognition technology, which identifies or verifies a certain individual is absolutely essential under robotic environments for intelligent services. The conventional face recognition algorithm using single distance face image as training images has a problem that face recognition rate decreases as distance increases. The face recognition algorithm using face images by actual distance as training images shows good performance but this has a problem that it requires user cooperation. This paper proposes the LDA-based long distance face recognition method which uses multiple distance face images from a zoom camera for training face images. The proposed face recognition technique generated better performance by average 7.8% than the technique using the existing single distance face image as training. Compared with the technique that used face images by distance as training, the performance fell average 8.0%. However, the proposed method has a strength that it spends less time and requires less cooperation to users when taking face images.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.1
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• pp.39-45
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• 2019

We propose an active color model based method for tracking motions of multiple human using a networked multiple-camera system in IoT space as a human-robot coexistent system. An IoT space is a space where many intelligent devices, such as computers and sensors(color CCD cameras for example), are distributed. Human beings can be a part of IoT space as well. One of the main goals of IoT space is to assist humans and to do different services for them. In order to be capable of doing that, IoT space must be able to do different human related tasks. One of them is to identify and track multiple objects seamlessly. In the environment where many camera modules are distributed on network, it is important to identify object in order to track it, because different cameras may be needed as object moves throughout the space and IoT space should determine the appropriate one. This paper describes appearance based unknown object tracking with the distributed vision system in IoT space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.361-364
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• 1997

There has been requested to automate the welding process in shipyard due to its dependence on skilled operators and the inferior working environments. According to these demands, multiple robot welding system for sub-assembly welding line has been developed, realized and installed at Keoje Shipyard. In order to realize automatic welding system, robots have to be equipped with the sensing system to recognize the position of the welding panels. In this research, a camera vision system is developed to detect the position of base panels for subassembly line in shipbuilding. Two camera vision systems are used in two different stages (Mounting and Welding) to automate the recognition and positioning of welding lines. For automatic recognition of panel position, various image processing algorithms are proposed in this paper.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.436-442
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• 2016

There are a lot of sports when you left the victory and the defeat of the match the referee subjective judgment. In particular, TaeKwonDo pumse How accurate a given action? Is important. Objectively evaluate the subjective opinion of victory and defeat in a sporting event and the technology to keep as evidence is required. This study was implemented a system for recognizing Taekwondo executed through the number of motion recognition device. Step Sensor also used to detect a user's location. This study evaluated the rate matching the standard gesture data and the motion data. Through multiple gesture recognition equipment was more accurate assessment of the Taekwondo action.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1202-1205
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• 2005

This paper describes a method for vision-based person identification that can detect, track, and recognize person from video using multiple cues: height and dressing colors. The method does not require constrained target's pose or fully frontal face image to identify the person. First, the system, which is connected to a pan-tilt-zoom camera, detects target using motion detection and human cardboard model. The system keeps tracking the moving target while it is trying to identify whether it is a human and identify who it is among the registered persons in the database. To segment the moving target from the background scene, we employ a version of background subtraction technique and some spatial filtering. Once the target is segmented, we then align the target with the generic human cardboard model to verify whether the detected target is a human. If the target is identified as a human, the card board model is also used to segment the body parts to obtain some salient features such as head, torso, and legs. The whole body silhouette is also analyzed to obtain the target's shape information such as height and slimness. We then use these multiple cues (at present, we uses shirt color, trousers color, and body height) to recognize the target using a supervised self-organization process. We preliminary tested the system on a set of 5 subjects with multiple clothes. The recognition rate is 100% if the person is wearing the clothes that were learned before. In case a person wears new dresses the system fail to identify. This means height is not enough to classify persons. We plan to extend the work by adding more cues such as skin color, and face recognition by utilizing the zoom capability of the camera to obtain high resolution view of face; then, evaluate the system with more subjects.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.47-57
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• 2024

This paper proposes a system to efficiently manage delays caused by unmanaged and congested queues in crowded environments. Such queues not only cause inconvenience but also pose safety risks. Existing systems, relying on single-camera feeds, are inadequate for complex scenarios requiring multiple cameras. To address this, we developed a multi-vision long queue detection system that integrates multiple vision algorithms to accurately detect various types of queues. The algorithm processes real-time video data from multiple cameras, stitching overlapping segments into a single panoramic image. By combining object detection, tracking, and position variation analysis, the system recognizes long queues in crowded environments. The algorithm was validated with 96% accuracy and a 92% F1-score across diverse settings.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.3
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• pp.344-352
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• 1999

There has been requested to automate the welding process in shipyard due to its dependence on skilled operators and the inferior working environments. According to these demands, multiple robot welding system for sub-assembly welding line has been developed, realized and installed at Keoje shipyard. In order to realize automatic welding system, robots have to be equipped with a sensing system to recognize the position of the welding panels. In this research, a camera vision system(CVS) is developed to detect the position of base panels for sub-assembly line in shipbuilding. Two camera vision systems are used in two different stages (fitting and welding) to automate the recognition and positioning of welding lines. For automatic recognition of panel position, various image processing algorithms are proposed in this paper.

• Journal of the Korea Society of Computer and Information
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• v.24 no.2
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• pp.119-126
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• 2019

As interest in augmented reality has increased recently, attempts have been made to incorporate augmented reality into various fields. In implementing augmented reality, the method by which markers are used is to extract feature points of markers to recognize 3D coordinates and, in some cases, it is necessary to recognize multiple markers simultaneously. Therefore, this paper proposes optimization methods for recognising multiple markers at the same time. Unity 3D and augmented reality library Vuforia are used to implement the experimental environment. The augmented reality program produced was implemented in an application form and tested using a mobile camera. We looked for optimization methods for manufacturing markers directly and for recognizing multiple markers through changes in the experimental environment. The results of the experiment can provide a higher recognition rate in an environment where multiple marker recognition is required later.

• Nuclear Engineering and Technology
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• v.42 no.6
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• pp.704-711
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• 2010

Modal testing requires measuring the vibration of many points, for which an accelerometer, a gab sensor and laser vibrometer are generally used. Conventional modal testing requires mounting of these sensors to all measurement points in order to acquire the signals. However, this can be disadvantageous because it requires considerable measurement time and effort when there are many measurement points. In this paper, we propose a method for modal testing using a camera image. A camera can measure the vibration of many points at the same time. However, this task requires that the measurement points be classified frame by frame. While it is possible to classify the measurement points one by one, this also requires much time. Therefore, we try to classify multiple points using pattern recognition. The feasibility of the proposed method is verified by a beam experiment. The experimental results demonstrate that we can obtain good results.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.100.4-100
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• 2002

For visual navigation, mobile robot can use a stereo camera which has large field of view. In this paper, we propose an algorithm to detect and recognize human face on the basis of such camera system. In this paper, a new coarse to fine detection algorithm is proposed. For coarse detection, nearly face-like areas are found in entire image using dual ellipse templates. And, detailed alignment of facial outline and features is performed on the basis of view- based multiple appearance model. Because it hard to finely align with facial features in this case, we try to find most resembled face image area is selected from multiple face appearances using most distinguished facial features- two eye...