• 제목/요약/키워드: Natural hand gesture

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다문화 손동작 인식을 위한 HOG-HOD 알고리즘 (HOG-HOD Algorithm for Recognition of Multi-cultural Hand Gestures)

  • 김지예;박종일
    • 한국멀티미디어학회논문지
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    • 제20권8호
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    • pp.1187-1199
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    • 2017
  • In recent years, research about Natural User Interface (NUI) has become focused because NUI system can give natural feelings for users in virtual reality. Most important thing in NUI system is how to communicate with the computer system. There are many things to interact with users such as speech, hand gestures, body actions. Among them, hand gesture is suitable for the purpose of NUI because people often use a relatively high frequency in daily life and hand gesture have meaning only by itself. This hand gestures called multi-cultural hand gesture and we proposed the method to recognize this kind of hand gestures. Proposed method is composed of Histogram of Oriented Gradients (HOG) used for hand shape recognition and Histogram of Oriented Displacements (HOD) used for hand center point trajectory recognition.

A Memory-efficient Hand Segmentation Architecture for Hand Gesture Recognition in Low-power Mobile Devices

  • Choi, Sungpill;Park, Seongwook;Yoo, Hoi-Jun
    • JSTS:Journal of Semiconductor Technology and Science
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    • 제17권3호
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    • pp.473-482
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    • 2017
  • Hand gesture recognition is regarded as new Human Computer Interaction (HCI) technologies for the next generation of mobile devices. Previous hand gesture implementation requires a large memory and computation power for hand segmentation, which fails to give real-time interaction with mobile devices to users. Therefore, in this paper, we presents a low latency and memory-efficient hand segmentation architecture for natural hand gesture recognition. To obtain both high memory-efficiency and low latency, we propose a streaming hand contour tracing unit and a fast contour filling unit. As a result, it achieves 7.14 ms latency with only 34.8 KB on-chip memory, which are 1.65 times less latency and 1.68 times less on-chip memory, respectively, compare to the best-in-class.

The Effect of Visual Feedback on One-hand Gesture Performance in Vision-based Gesture Recognition System

  • Kim, Jun-Ho;Lim, Ji-Hyoun;Moon, Sung-Hyun
    • 대한인간공학회지
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    • 제31권4호
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    • pp.551-556
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    • 2012
  • Objective: This study presents the effect of visual feedback on one-hand gesture performance in vision-based gesture recognition system when people use gestures to control a screen device remotely. Backgroud: gesture interaction receives growing attention because it uses advanced sensor technology and it allows users natural interaction using their own body motion. In generating motion, visual feedback has been to considered critical factor affect speed and accuracy. Method: three types of visual feedback(arrow, star, and animation) were selected and 20 gestures were listed. 12 participants perform each 20 gestures while given 3 types of visual feedback in turn. Results: People made longer hand trace and take longer time to make a gesture when they were given arrow shape feedback than star-shape feedback. The animation type feedback was most preferred. Conclusion: The type of visual feedback showed statistically significant effect on the length of hand trace, elapsed time, and speed of motion in performing a gesture. Application: This study could be applied to any device that needs visual feedback for device control. A big feedback generate shorter length of motion trace, less time, faster than smaller one when people performs gestures to control a device. So the big size of visual feedback would be recommended for a situation requiring fast actions. On the other hand, the smaller visual feedback would be recommended for a situation requiring elaborated actions.

강인한 손가락 끝 추출과 확장된 CAMSHIFT 알고리즘을 이용한 자연스러운 Human-Robot Interaction을 위한 손동작 인식 (A Robust Fingertip Extraction and Extended CAMSHIFT based Hand Gesture Recognition for Natural Human-like Human-Robot Interaction)

  • 이래경;안수용;오세영
    • 제어로봇시스템학회논문지
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    • 제18권4호
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    • pp.328-336
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    • 2012
  • In this paper, we propose a robust fingertip extraction and extended Continuously Adaptive Mean Shift (CAMSHIFT) based robust hand gesture recognition for natural human-like HRI (Human-Robot Interaction). Firstly, for efficient and rapid hand detection, the hand candidate regions are segmented by the combination with robust $YC_bC_r$ skin color model and haar-like features based adaboost. Using the extracted hand candidate regions, we estimate the palm region and fingertip position from distance transformation based voting and geometrical feature of hands. From the hand orientation and palm center position, we find the optimal fingertip position and its orientation. Then using extended CAMSHIFT, we reliably track the 2D hand gesture trajectory with extracted fingertip. Finally, we applied the conditional density propagation (CONDENSATION) to recognize the pre-defined temporal motion trajectories. Experimental results show that the proposed algorithm not only rapidly extracts the hand region with accurately extracted fingertip and its angle but also robustly tracks the hand under different illumination, size and rotation conditions. Using these results, we successfully recognize the multiple hand gestures.

A Decision Tree based Real-time Hand Gesture Recognition Method using Kinect

  • Chang, Guochao;Park, Jaewan;Oh, Chimin;Lee, Chilwoo
    • 한국멀티미디어학회논문지
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    • 제16권12호
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    • pp.1393-1402
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    • 2013
  • Hand gesture is one of the most popular communication methods in everyday life. In human-computer interaction applications, hand gesture recognition provides a natural way of communication between humans and computers. There are mainly two methods of hand gesture recognition: glove-based method and vision-based method. In this paper, we propose a vision-based hand gesture recognition method using Kinect. By using the depth information is efficient and robust to achieve the hand detection process. The finger labeling makes the system achieve pose classification according to the finger name and the relationship between each fingers. It also make the classification more effective and accutate. Two kinds of gesture sets can be recognized by our system. According to the experiment, the average accuracy of American Sign Language(ASL) number gesture set is 94.33%, and that of general gestures set is 95.01%. Since our system runs in real-time and has a high recognition rate, we can embed it into various applications.

주거 공간에서의 3차원 핸드 제스처 인터페이스에 대한 사용자 요구사항 (User Needs of Three Dimensional Hand Gesture Interfaces in Residential Environment Based on Diary Method)

  • 정동영;김희진;한성호;이동훈
    • 대한산업공학회지
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    • 제41권5호
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    • pp.461-469
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    • 2015
  • The aim of this study is to find out the user's needs of a 3D hand gesture interface in the smart home environment. To find out the users' needs, we investigated which object the users want to use with a 3D hand gesture interface and why they want to use a 3D hand gesture interface. 3D hand gesture interfaces are studied to be applied to various devices in the smart environment. 3D hand gesture interfaces enable the users to control the smart environment with natural and intuitive hand gestures. With these advantages, finding out the user's needs of a 3D hand gesture interface would improve the user experience of a product. This study was conducted using a diary method to find out the user's needs with 20 participants. They wrote the needs of a 3D hand gesture interface during one week filling in the forms of a diary. The form of the diary is comprised of who, when, where, what and how to use a 3D hand gesture interface with each consisting of a usefulness score. A total of 322 data (209 normal data and 113 error data) were collected from users. There were some common objects which the users wanted to control with a 3D hand gesture interface and reasons why they want to use a 3D hand gesture interface. Among them, the users wanted to use a 3D hand gesture interface mostly to control the light, and to use a 3D hand gesture interface mostly to overcome hand restrictions. The results of this study would help develop effective and efficient studies of a 3D hand gesture interface giving valuable insights for the researchers and designers. In addition, this could be used for creating guidelines for 3D hand gesture interfaces.

다변량 퍼지 의사결정트리와 사용자 적응을 이용한 손동작 인식 (Hand Gesture Recognition using Multivariate Fuzzy Decision Tree and User Adaptation)

  • 전문진;도준형;이상완;박광현;변증남
    • 로봇학회논문지
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    • 제3권2호
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    • pp.81-90
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    • 2008
  • While increasing demand of the service for the disabled and the elderly people, assistive technologies have been developed rapidly. The natural signal of human such as voice or gesture has been applied to the system for assisting the disabled and the elderly people. As an example of such kind of human robot interface, the Soft Remote Control System has been developed by HWRS-ERC in $KAIST^[1]$. This system is a vision-based hand gesture recognition system for controlling home appliances such as television, lamp and curtain. One of the most important technologies of the system is the hand gesture recognition algorithm. The frequently occurred problems which lower the recognition rate of hand gesture are inter-person variation and intra-person variation. Intra-person variation can be handled by inducing fuzzy concept. In this paper, we propose multivariate fuzzy decision tree(MFDT) learning and classification algorithm for hand motion recognition. To recognize hand gesture of a new user, the most proper recognition model among several well trained models is selected using model selection algorithm and incrementally adapted to the user's hand gesture. For the general performance of MFDT as a classifier, we show classification rate using the benchmark data of the UCI repository. For the performance of hand gesture recognition, we tested using hand gesture data which is collected from 10 people for 15 days. The experimental results show that the classification and user adaptation performance of proposed algorithm is better than general fuzzy decision tree.

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A Vision-Based Method to Find Fingertips in a Closed Hand

  • Chaudhary, Ankit;Vatwani, Kapil;Agrawal, Tushar;Raheja, J.L.
    • Journal of Information Processing Systems
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    • 제8권3호
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    • pp.399-408
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    • 2012
  • Hand gesture recognition is an important area of research in the field of Human Computer Interaction (HCI). The geometric attributes of the hand play an important role in hand shape reconstruction and gesture recognition. That said, fingertips are one of the important attributes for the detection of hand gestures and can provide valuable information from hand images. Many methods are available in scientific literature for fingertips detection with an open hand but very poor results are available for fingertips detection when the hand is closed. This paper presents a new method for the detection of fingertips in a closed hand using the corner detection method and an advanced edge detection algorithm. It is important to note that the skin color segmentation methodology did not work for fingertips detection in a closed hand. Thus the proposed method applied Gabor filter techniques for the detection of edges and then applied the corner detection algorithm for the detection of fingertips through the edges. To check the accuracy of the method, this method was tested on a vast number of images taken with a webcam. The method resulted in a higher accuracy rate of detections from the images. The method was further implemented on video for testing its validity on real time image capturing. These closed hand fingertips detection would help in controlling an electro-mechanical robotic hand via hand gesture in a natural way.

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

  • 윤민호;김윤제;김태영
    • 한국게임학회 논문지
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    • 제17권6호
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    • pp.113-120
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    • 2017
  • 최근 가상현실 기술의 발전으로 가상의 3D 객체와 자연스러운 상호작용이 가능하도록 하는 사용자 친화적인 손 제스처 인터페이스에 대한 연구가 활발히 진행되고 있다. 그러나 대부분의 연구는 단순하고 적은 종류의 손 제스처만 지원되고 있는 실정이다. 본 논문은 가상환경에서 3D 객체와 보다 직관적인 방식의 손 제스처 인터페이스 방법을 제안한다. 손 제스처 인식을 위하여 먼저 전처리 과정을 거친 다양한 손 데이터를 이진 결정트리로 1차 분류를 한다. 분류된 데이터는 리샘플링을 한 다음 체인코드를 생성하고 이에 대한 히스토그램으로 특징 데이터를 구성한다. 이를 기반으로 학습된 MCSVM을 통해 2차 분류를 수행하여 제스처를 인식한다. 본 방법의 검증을 위하여 3D 블록을 손 제스처를 통하여 조작하는 'Virtual Block'이라는 게임을 구현하여 실험한 결과 16개의 제스처에 대해 99.2%의 인식률을 보였으며 기존의 인터페이스보다 직관적이고 사용자 친화적임을 알 수 있었다.

자연스런 손동작을 이용한 모바일 로봇의 동작제어 (Motion Control of a Mobile Robot Using Natural Hand Gesture)

  • 김아람;이상용
    • 한국지능시스템학회논문지
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    • 제24권1호
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    • pp.64-70
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    • 2014
  • 오늘날 일상생활에서 인간과 함께 생활하는 로봇들은 자연스러운 의사소통 방법이 요구된다. 따라서 기존의 단순한 로봇 제어 방식을 이용하여 제어하는 것 보다 실제 사람과 상호작용 하는 것과 같은 방식의 제어방식이 요구되고 있다. 기존의 연구들은 사람의 행동 자체를 인식하는 것에 초점이 맞추어져 있어서 자연스러운 의사소통을 하기 어렵다. 본 논문에서는 모바일 로봇을 제어하는 방법으로 자연스러운 손동작을 은닉 마르코프 모델(HMM: hidden markov model) 과 퍼지추론을 이용하는 방법을 제안한다. 키넥트 센서를 이용해 색상 데이터와 깊이 데이터를 획득하고 사람의 손을 검색하고 HMM과 Mamdani 퍼지추론을 이용하여 손동작을 인식한다. 인식된 결과를 로봇에게 전달하여 원하는 방향으로 이동시킨다.