• The Journal of Korean Institute for Practical Engineering Education
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• v.2 no.1
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• pp.84-88
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• 2010

Recently, it is essential to have the system which can track down and identity the random object in the space in which security is a high priority. Due to the fact that we mentioned above, in this paper. We suggest the intelligent video surveillance system effective image-process-education in this paper. The experiment was conducted to check and track down the entering vehicle. And, Pan-Tilt-Zoom camera was used to obtain the enlarged image of the object while a vehicle was making stop in target area. As a result, the experiment has shown the data as following. When the object is in motion, success rate is 97.4%, while success rate is 91% when the object is motionless. By using the suggested system, effective image-process-education is should be achieved because the students who participate in the class can have simultaneous access to the system for real time image data and camera control.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.135-144
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• 2018

The domestic pigsty environment is highly vulnerable to the spread of respiratory diseases such as foot-and-mouth disease because of the small space. In order to manage this issue, a variety of studies have been conducted to automatically analyze behavior of individual pigs in a pig pen through a video surveillance system using a camera. Even though it is required to correctly segment touching pigs for tracking each pig in complex situations such as aggressive behavior, detecting the correct boundaries among touching pigs using Kinect's depth information of lower accuracy is a challenging issue. In this paper, we propose a segmentation method using motion information of the touching pigs. In addition, our proposed method can be applied for detecting tracking errors in case of tracking individual pigs in the complex environment. In the experimental results, we confirmed that the touching pigs in a pig farm were separated with the accuracy of 86%, and also confirmed that the tracking errors were detected accurately.

• Journal of information and communication convergence engineering
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• v.5 no.4
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• pp.382-388
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• 2007

Indoor location system using wireless sensor network technology was applied for the status evaluation and activity monitoring of elderly person or chronic invalid at home. Location awareness application is transparent to the daily activities, while providing the embedded computing infrastructure with an awareness of what is happening in this space. To locate an object, the active ceiling-mounted reference beacons were placed throughout the building. Reference beacons periodically publish location information on RF and ultrasonic signals to allow application running on mobile or static nodes to study and determine their physical location. Once object-carried passive listener receives the information, it subsequently determines it's location from reference beacons. By using only the sensor nodes without any external network infrastructure the cost of the system was reduced while the accuracy in our experiments. was fairly good and fine grained between 7 and 15 cm for location awareness in indoor environments. Passive architecture used here provides the security of the user privacy while at the server the privacy was secured by providing the authentication using Geopriv approach. This information from sensor nodes is further forwarded to base station where further computation is performed to determine the current position of object and several applications are enabled for context awareness.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.158-163
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• 2009

In recent years, there has been increased interest in characterizing and extracting 3D information from 2D images for human tracking and identification. In this paper, we propose a single view-based framework for robust estimation of height and position. In the proposed method, 2D features of target object is back-projected into the 3D scene space where its coordinate system is given by a rectangular marker. Then the position and the height are estimated in the 3D space. In addition, geometric error caused by inaccurate projective mapping is corrected by using geometric constraints provided by the marker. The accuracy and the robustness of our technique are verified on the experimental results of several real video sequences from outdoor environments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.683-685
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• 2010

Augmented reality is a technology for showing virtual objects overlapped with the real world as seen from the eyes of users. Studies are being conducted on effective communication methods using hand movements in augmented reality. Hand movements are an important means of human communication along with writing and voice. Space arrangement technology and hand tracking technology using markers to acquire virtual information in augmented reality have many limitations. This paper proposes tracking hand area for augmented object. The augmented objects change viewpoints by hand positions which improves the sense of three dimensions and immersion.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.201-211
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• 2013

In this paper, we propose a hand tracking and gesture recognition system. Our system employs a depth capture device to obtain 3D geometric information of user's bare hand. In particular, we build a flexible tracking volume and restrict the hand tracking area, so that we can avoid diverse problems caused by conventional object detection/tracking systems. The proposed system computes running average of the hand position, and tracking volume is actively adjusted according to the statistical information that is computed on the basis of uncertainty of the user's hand motion in the 3D space. Once the position of user's hand is obtained, then the system attempts to detect stretched fingers to recognize finger gesture of the user's hand. In order to test the proposed framework, we built a NUI system using the proposed technique, and verified that our system presents very stable performance even in the case that multiple objects exist simultaneously in the crowded environment, as well as in the situation that the scene is occluded temporarily. We also verified that our system ensures running speed of 24-30 frames per second throughout the experiments.

• Spatial Information Research
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• v.13 no.2 s.33
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• pp.129-138
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• 2005

In this paper, a 3D position tracking algorithm for a moving objects using a stereo CCD cameras was proposed. This paper purposed the method to extract the coordinates of the moving objects. That is improve the operating and data processing efficiency. We were applied the relative orientation far the stereo CCD cameras and image coordinates extraction in the left and right images after the moving object segmentation. Also, it is decided on 3D position far moving objects using an acquired image coordinates in the left and right images. We were used independent relative orientation to decide the relative location and attitude of the stereo CCD cameras and RGB pixel values to segment the moving objects. To calculate the coordinates of the moving objects by space intersection. And, We conducted the experiment the system and compared the accuracy of the results.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.9
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• pp.794-806
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• 2017

It is essential to protect the national space assets and space environment safely as a space development country from the continuously increasing space debris. And Active Debris Removal(ADR) is the most active way to solve this problem. In this paper, we studied the Artificial Neural Network(ANN) for a stable recognition model of vision-based space debris tracking system. We obtained the simulated image of the space environment by the KARICAT which is the ground-based space debris clearing satellite testbed developed by the Korea Aerospace Research Institute, and created the vector which encodes structure and color-based features of each object after image segmentation by depth discontinuity. The Feature Vector consists of 3D surface area, principle vector of point cloud, 2D shape and color information. We designed artificial neural network model based on the separated Feature Vector. In order to improve the performance of the artificial neural network, the model is divided according to the categories of the input feature vectors, and the ensemble technique is applied to each model. As a result, we confirmed the performance improvement of recognition model by ensemble technique.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.75-82
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• 2022

There are various exhibition halls, shopping malls, theme parks around us and analysis of interest in exhibits or contents is mainly done through questionnaires. These questionnaires are mainly depend on the subjective memory of the person being investigated, resulting in incorrect statistical results. Therefore, it is possible to identify an exhibition space with low interest by tracking the movement and counting the number of visitors. Based on this, it can be used as quantitative data for exhibits that need replacement. In this paper, we use deep learning-based artificial intelligence algorithms to recognize visitors, assign IDs to the recognized visitors, and continuously track them to identify the movement path. When visitors pass the counting line, the system is designed to count the number and transmit data to the server for integrated management.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.393-398
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• 2002

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. forthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.