• Title/Summary/Keyword: 3D gesture recognition technology

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Development of Gesture Recognition-Based 3D Serious Games (치매 예방을 위한 제스처 인식 기반 3D 기능성 게임 개발)

  • He, Guan-Feng;Park, Jin-Woong;Kang, Sun-Kyung;Jung, Sung-Tae
    • Journal of Korea Game Society
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    • v.11 no.6
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    • pp.103-113
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    • 2011
  • In this paper, we propose gesture recognition based 3D Serious Games to prevent dementia. These games are designed to enhance the effect of preventing dementia by helping increase brain usage and physical activities of users by the entire body gesture recognition. The existing cameras used for gesture recognition technology are limited in terms of recognition ratio and operation range. For more stable recognition of the body gestures, we recognized users with a 3D depth camera, obtained joint data of users, and analyzed joint motions to recognize gestures of the body. Game contents were designed to practice memory, reasoning, calculation, and spatial recognition focusing on the atrophy of brain cells as a major cause of dementia. Game results of each user were saved and analyzed to measure how their recognition skills improved.

Gesture based Natural User Interface for e-Training

  • Lim, C.J.;Lee, Nam-Hee;Jeong, Yun-Guen;Heo, Seung-Il
    • Journal of the Ergonomics Society of Korea
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    • v.31 no.4
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    • pp.577-583
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    • 2012
  • Objective: This paper describes the process and results related to the development of gesture recognition-based natural user interface(NUI) for vehicle maintenance e-Training system. Background: E-Training refers to education training that acquires and improves the necessary capabilities to perform tasks by using information and communication technology(simulation, 3D virtual reality, and augmented reality), device(PC, tablet, smartphone, and HMD), and environment(wired/wireless internet and cloud computing). Method: Palm movement from depth camera is used as a pointing device, where finger movement is extracted by using OpenCV library as a selection protocol. Results: The proposed NUI allows trainees to control objects, such as cars and engines, on a large screen through gesture recognition. In addition, it includes the learning environment to understand the procedure of either assemble or disassemble certain parts. Conclusion: Future works are related to the implementation of gesture recognition technology for a multiple number of trainees. Application: The results of this interface can be applied not only in e-Training system, but also in other systems, such as digital signage, tangible game, controlling 3D contents, etc.

Implementation of Non-Contact Gesture Recognition System Using Proximity-based Sensors

  • Lee, Kwangjae
    • Journal of the Semiconductor & Display Technology
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    • v.19 no.3
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    • pp.106-111
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    • 2020
  • In this paper, we propose the non-contact gesture recognition system and algorithm using proximity-based sensors. The system uses four IR receiving photodiode embedded on a single chip and an IR LED for small area. The goal of this paper is to use the proposed algorithm to solve the problem associated with bringing the four IR receivers close to each other and to implement a gesture sensor capable of recognizing eight directional gestures from a distance of 10cm and above. The proposed system was implemented on a FPGA board using Verilog HDL with Android host board. As a result of the implementation, a 2-D swipe gesture of fingers and palms of 3cm and 15cm width was recognized, and a recognition rate of more than 97% was achieved under various conditions. The proposed system is a low-power and non-contact HMI system that recognizes a simple but accurate motion. It can be used as an auxiliary interface to use simple functions such as calls, music, and games for portable devices using batteries.

AdaBoost-Based Gesture Recognition Using Time Interval Trajectory Features (시간 간격 특징 벡터를 이용한 AdaBoost 기반 제스처 인식)

  • Hwang, Seung-Jun;Ahn, Gwang-Pyo;Park, Seung-Je;Baek, Joong-Hwan
    • Journal of Advanced Navigation Technology
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    • v.17 no.2
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    • pp.247-254
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    • 2013
  • The task of 3D gesture recognition for controlling equipments is highly challenging due to the propagation of 3D smart TV recently. In this paper, the AdaBoost algorithm is applied to 3D gesture recognition by using Kinect sensor. By tracking time interval trajectory of hand, wrist and arm by Kinect, AdaBoost algorithm is used to train and classify 3D gesture. Experimental results demonstrate that the proposed method can successfully extract trained gestures from continuous hand, wrist and arm motion in real time.

A Study of the Physical Experience Using Serious Game Design Traffic Safety Education for Children applied using 3D Depth Gesture Recognition Technology (3차원 동작인식기술을 적용한 어린이 교통안전교육 체감형 기능성 게임디자인 연구)

  • Jang, Chang-Ik
    • Journal of Korea Game Society
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    • v.12 no.6
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    • pp.5-14
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    • 2012
  • The purpose of this paper is to demonstrate how three-dimensional gesture recognition technology, in children's traffic safety programs, can be an effective solution for instructing children in the safest ways to interact with traffic. In terms of traffic accidents, walking unaccompanied is the most dangerous traffic related activity for children. By using a three-dimensional serous game training program that implements gesture recognition, more accurate real life scenarios can be implemented in existing children's traffic training programs. The implementation of this technology will increase the possibility of changing the habits and attitudes of children, which in turn will lower the amount of walking related traffic accidents in children.

Virtual Block Game Interface based on the Hand Gesture Recognition (손 제스처 인식에 기반한 Virtual Block 게임 인터페이스)

  • Yoon, Min-Ho;Kim, Yoon-Jae;Kim, Tae-Young
    • Journal of Korea Game Society
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    • v.17 no.6
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    • pp.113-120
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    • 2017
  • With the development of virtual reality technology, in recent years, user-friendly hand gesture interface has been more studied for natural interaction with a virtual 3D object. Most earlier studies on the hand-gesture interface are using relatively simple hand gestures. In this paper, we suggest an intuitive hand gesture interface for interaction with 3D object in the virtual reality applications. For hand gesture recognition, first of all, we preprocess various hand data and classify the data through the binary decision tree. The classified data is re-sampled and converted to the chain-code, and then constructed to the hand feature data with the histograms of the chain code. Finally, the input gesture is recognized by MCSVM-based machine learning from the feature data. To test our proposed hand gesture interface we implemented a 'Virtual Block' game. Our experiments showed about 99.2% recognition ratio of 16 kinds of command gestures and more intuitive and user friendly than conventional mouse interface.

Dual Autostereoscopic Display Platform for Multi-user Collaboration with Natural Interaction

  • Kim, Hye-Mi;Lee, Gun-A.;Yang, Ung-Yeon;Kwak, Tae-Jin;Kim, Ki-Hong
    • ETRI Journal
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    • v.34 no.3
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    • pp.466-469
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    • 2012
  • In this letter, we propose a dual autostereoscopic display platform employing a natural interaction method, which will be useful for sharing visual data with users. To provide 3D visualization of a model to users who collaborate with each other, a beamsplitter is used with a pair of autostereoscopic displays, providing a visual illusion of a floating 3D image. To interact with the virtual object, we track the user's hands with a depth camera. The gesture recognition technique we use operates without any initialization process, such as specific poses or gestures, and supports several commands to control virtual objects by gesture recognition. Experiment results show that our system performs well in visualizing 3D models in real-time and handling them under unconstrained conditions, such as complicated backgrounds or a user wearing short sleeves.

Design and Implementation of a Stereoscopic Image Control System based on User Hand Gesture Recognition (사용자 손 제스처 인식 기반 입체 영상 제어 시스템 설계 및 구현)

  • Song, Bok Deuk;Lee, Seung-Hwan;Choi, HongKyw;Kim, Sung-Hoon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.26 no.3
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    • pp.396-402
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    • 2022
  • User interactions are being developed in various forms, and in particular, interactions using human gestures are being actively studied. Among them, hand gesture recognition is used as a human interface in the field of realistic media based on the 3D Hand Model. The use of interfaces based on hand gesture recognition helps users access media media more easily and conveniently. User interaction using hand gesture recognition should be able to view images by applying fast and accurate hand gesture recognition technology without restrictions on the computer environment. This paper developed a fast and accurate user hand gesture recognition algorithm using the open source media pipe framework and machine learning's k-NN (K-Nearest Neighbor). In addition, in order to minimize the restriction of the computer environment, a stereoscopic image control system based on user hand gesture recognition was designed and implemented using a web service environment capable of Internet service and a docker container, a virtual environment.

A Framework for 3D Hand Gesture Design and Modeling (삼차원 핸드 제스쳐 디자인 및 모델링 프레임워크)

  • Kwon, Doo-Young
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.14 no.10
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    • pp.5169-5175
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    • 2013
  • We present a framework for 3D hand gesture design and modeling. We adapted two different pattern matching techniques, Dynamic Time Warping (DTW) and Hidden Markov Models (HMMs), to support the registration and evaluation of 3D hand gestures as well as their recognition. One key ingredient of our framework is a concept for the convenient gesture design and registration using HMMs. DTW is used to recognize hand gestures with a limited training data, and evaluate how the performed gesture is similar to its template gesture. We facilitate the use of visual sensors and body sensors for capturing both locative and inertial gesture information. In our experimental evaluation, we designed 18 example hand gestures and analyzed the performance of recognition methods and gesture features under various conditions. We discuss the variability between users in gesture performance.

An Efficient Hand Gesture Recognition Method using Two-Stream 3D Convolutional Neural Network Structure (이중흐름 3차원 합성곱 신경망 구조를 이용한 효율적인 손 제스처 인식 방법)

  • Choi, Hyeon-Jong;Noh, Dae-Cheol;Kim, Tae-Young
    • The Journal of Korean Institute of Next Generation Computing
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    • v.14 no.6
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    • pp.66-74
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    • 2018
  • Recently, there has been active studies on hand gesture recognition to increase immersion and provide user-friendly interaction in a virtual reality environment. However, most studies require specialized sensors or equipment, or show low recognition rates. This paper proposes a hand gesture recognition method using Deep Learning technology without separate sensors or equipment other than camera to recognize static and dynamic hand gestures. First, a series of hand gesture input images are converted into high-frequency images, then each of the hand gestures RGB images and their high-frequency images is learned through the DenseNet three-dimensional Convolutional Neural Network. Experimental results on 6 static hand gestures and 9 dynamic hand gestures showed an average of 92.6% recognition rate and increased 4.6% compared to previous DenseNet. The 3D defense game was implemented to verify the results of our study, and an average speed of 30 ms of gesture recognition was found to be available as a real-time user interface for virtual reality applications.