• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.7-10
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• 2008

자동차의 수가 점점 증가함에 따라 교통사고도 그 만큼 증가하고 있다. 교통사고의 주요 원인 중 하나가 졸음운전이나 부주의한 운전에 의한 것이다. 따라서 Real-Time으로 운전자의 제스처를 인식하여 졸음운전이나 부주의에 의한 사고를 사전에 예방하여 보다 안전한 운전을 돕는 서비스가 필요시 되고 있다. 본 논문에서는 운전자의 제스처 인식에 전처리 과정으로 운전자의 상반신에 대한 영상데이터에서 Adaboost를 이용하여 복잡한 배경과 다양한 환경에서 강인하게 얼굴 영역을 찾는 알고리즘을 소개한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.596-598
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• 2019

손동작 인식을 기반으로 한 Virtual fitting 시스템은 Kinect Sensor 를 사용하여 자연스러운 Fitting 을 구현할 수 있다. Kinect Sensor 를 이용한 Pose estimation, Gesture recognition, Virtual fitting 을 구현함으로써 가상으로 의복을 착용하는 소프트웨어를 소개한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1770-1776
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• 2021

For hand expression recognition, hand pose recognition based on the static shape of the hand and hand gesture recognition based on hand movement are used together. In this paper, we proposed a hand expression recognition method that recognizes symbols based on the trajectory of a hand movement on a virtual blackboard. In order to recognize a sign drawn by hand on a virtual blackboard, not only a method of recognizing a sign from a hand movement, but also hand pose recognition for finding the start and end of data input is also required. In this paper, MediaPipe was used to recognize hand pose, and LSTM(Long Short Term Memory), a type of recurrent neural network, was used to recognize hand gesture from time series data. To verify the effectiveness of the proposed method, it was applied to the recognition of numbers written on a virtual blackboard, and a recognition rate of about 94% was obtained.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.221-225
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• 2010

This paper presents the preliminary study of an interactive rehabilitation support system for both dementia patients and their caregivers, the goal of which is to improve the quality of life(QOL) of the patients suffering from dementia through virtual interaction. To achieve the virtual interaction, three kinds of recognition modules for speech, facial image and pen-mouse gesture are studied. The results of both practical tests and questionnaire surveys show that the proposed system had to be further improved, especially in both speech recognition and user interface for real-world applications. The surveys also revealed that the pen-mouse gesture recognition, as one of possible interactive aids, show us a probability to support weakness of speech recognition.

• Journal of Integrative Natural Science
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• v.5 no.2
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• pp.120-126
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• 2012

This paper describes the algorithm that lowers the dimension, maintains the gesture recognition and significantly reduces the eigenspace configuration time by combining the higher correlation feature information and Principle Component Analysis. Since the suggested method doesn't require a lot of computation than the method using existing geometric information or stereo image, the fact that it is very suitable for building the real-time system has been proved through the experiment. In addition, since the existing point to point method which is a simple distance calculation has many errors, in this paper to improve recognition rate the recognition error could be reduced by using several successive input images as a unit of recognition with K-Nearest Neighbor which is the improved Class to Class method.

• The Journal of the Korea Contents Association
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• v.10 no.8
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• pp.11-17
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• 2010

In this paper, we propose HMM-based upper-body gesture. First, to recognize gesture of space, division about pose that is composing gesture once should be put priority. In order to divide poses which using interface, we used two IR cameras established on front side and side. So we can divide and acquire in front side pose and side pose about one pose in each IR camera. We divided the acquired IR pose image using SVM's non-linear RBF kernel function. If we use RBF kernel, we can divide misclassification between non-linear classification poses. Like this, sequences of divided poses is recognized by gesture using HMM's state transition matrix. The recognized gesture can apply to existent application to do mapping to OS Value.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.621-625
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• 2010

This paper presents a hand motion tracking method for hand gesture interface using a camera in mobile devices such as a smart phone and PDA. When a camera moves according to the hand gesture of the user, global optical flows are generated. Therefore, robust hand movement estimation is possible by considering dominant optical flow based on histogram analysis of the motion direction. A continuous hand gesture is segmented into unit gestures by motion state estimation using motion phase, which is determined by velocity and acceleration of the estimated hand motion. Feature vectors are extracted during movement states and hand gestures are recognized at the end state of each gesture. Support vector machine (SVM), k-nearest neighborhood classifier, and normal Bayes classifier are used for classification. SVM shows 82% recognition rate for 14 hand gestures.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.593-599
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• 2012

Objective: This study aims to implement a non-contact gesture-based interface for presentation purposes and to analyze the effect of the proposed interface as information transfer assisted device. Background: Recently, research on control device using gesture recognition or speech recognition is being conducted with rapid technological growth in UI/UX area and appearance of smart service products which requires a new human-machine interface. However, few quantitative researches on practical effects of the new interface type have been done relatively, while activities on system implementation are very popular. Method: The system presented in this study is implemented with KINECT$^{(R)}$ sensor offered by Microsoft Corporation. To investigate whether the proposed system is effective as a presentation support tool or not, we conduct experiments by giving several lectures to 40 participants in both a traditional lecture room(keyboard-based presentation control) and a non-contact gesture-based lecture room(KINECT-based presentation control), evaluating their interests and immersion based on contents of the lecture and lecturing methods, and analyzing their understanding about contents of the lecture. Result: We check that whether the gesture-based presentation system can play effective role as presentation supporting tools or not depending on the level of difficulty of contents using ANOVA. Conclusion: We check that a non-contact gesture-based interface is a meaningful tool as a sportive device when delivering easy and simple information. However, the effect can vary with the contents and the level of difficulty of information provided. Application: The results presented in this paper might help to design a new human-machine(computer) interface for communication support tools.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.723-730
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• 2013

This paper presents the design of an arm gesture recognition system using Kinect sensor. A variety of methods have been proposed for gesture recognition, ranging from the use of Dynamic Time Warping(DTW) to Hidden Markov Models(HMM). Our system learns a unique HMM corresponding to each arm gesture from a set of sequential skeleton data. Whenever the same gesture is performed, the trajectory of each joint captured by Kinect sensor may much differ from the previous, depending on the length and/or the orientation of the subject's arm. In order to obtain the robust performance independent of these conditions, the proposed system executes the feature transformation, in which the feature vectors of joint positions are transformed into those of angles between joints. To improve the computational efficiency for learning and using HMMs, our system also performs the k-means clustering to get one-dimensional integer sequences as inputs for discrete HMMs from high-dimensional real-number observation vectors. The dimension reduction and discretization can help our system use HMMs efficiently to recognize gestures in real-time environments. Finally, we demonstrate the recognition performance of our system through some experiments using two different datasets.

• Journal of the Ergonomics Society of Korea
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• v.36 no.2
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• pp.109-121
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• 2017

Objective: This study aims to find suitable types and styles for gesture interaction as remote control on smart TVs. Background: Smart TV is being developed rapidly in the world, and gesture interaction has a wide range of research areas, especially based on vision techniques. However, most studies are focused on the gesture recognition technology. Also, not many previous studies of gestures types and styles on smart TVs were carried out. Therefore, it is necessary to check what users prefer in terms of gesture types and styles for each operation command. Method: We conducted an experiment to extract the target user manipulation commands required for smart TVs and select the corresponding gestures. To do this, we looked at gesture styles people use for every operation command, and checked whether there are any gesture styles they prefer over others. Through these results, this study was carried out with a process selecting smart TV operation commands and gestures. Results: Eighteen TV commands have been used in this study. With agreement level as a basis, we compared the six types of gestures and five styles of gestures for each command. As for gesture type, participants generally preferred a gesture of Path-Moving type. In the case of Pan and Scroll commands, the highest agreement level (1.00) of 18 commands was shown. As for gesture styles, the participants preferred a manipulative style in 11 commands (Next, Previous, Volume up, Volume down, Play, Stop, Zoom in, Zoom out, Pan, Rotate, Scroll). Conclusion: By conducting an analysis on user-preferred gestures, nine gesture commands are proposed for gesture control on smart TVs. Most participants preferred Path-Moving type and Manipulative style gestures based on the actual operations. Application: The results can be applied to a more advanced form of the gestures in the 3D environment, such as a study on VR. The method used in this study will be utilized in various domains.