• Title/Summary/Keyword: Hand Tracking

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Interactive Data Acquisition System based on Hand Tracking to evaluate Children's Cognitive Abilities

  • Ekaterina, Ten;Lee, Suk-Ho
    • International Journal of Internet, Broadcasting and Communication
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    • v.14 no.3
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    • pp.108-114
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    • 2022
  • Autism (ASD) is a mental disorder characterized by a pronounced deficit in personal, social, speech, and other aspects of development and communication skills. Since autism is a complex developmental disorder that requires a lot of effort to recognize, this research was conducted to develop an interactive data Acquisition System and detect the first signs of ASD in children. The proposed system presents several variants of the tasks in an entertaining form, using hand tracking. Hand tracking is used to attract children's attention and interest them more to achieve more accurate results. The creation of the system is based on such libraries as OpenCV, PyGame, TensorFlow, and Mediapipe. The ultimate goal of the paper is to obtain data on the disease of autism in children for use in further diagnosis by medical experts.

A Long-Range Touch Interface for Interaction with Smart TVs

  • Lee, Jaeyeon;Kim, DoHyung;Kim, Jaehong;Cho, Jae-Il;Sohn, Joochan
    • ETRI Journal
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    • v.34 no.6
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    • pp.932-941
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    • 2012
  • A powerful interaction mechanism is one of the key elements for the success of smart TVs, which demand far more complex interactions than traditional TVs. This paper proposes a novel interface based on the famous touch interaction model but utilizes long-range bare hand tracking to emulate touch actions. To satisfy the essential requirements of high accuracy and immediate response, the proposed hand tracking algorithm adopts a fast color-based tracker but with modifications to avoid the problems inherent to those algorithms. By using online modeling and motion information, the sensitivity to the environment can be greatly decreased. Furthermore, several ideas to solve the problems often encountered by users interacting with smart TVs are proposed, resulting in a very robust hand tracking algorithm that works superbly, even for users with sleeveless clothing. In addition, the proposed algorithm runs at a very high speed of 82.73 Hz. The proposed interface is confirmed to comfortably support most touch operations, such as clicks, swipes, and drags, at a distance of three meters, which makes the proposed interface a good candidate for interaction with smart TVs.

Parameter Identification of Robot Hand Tracking Model Using Optimization (최적화 기법을 이용한 로봇핸드 트래킹 모델의 파라미터 추정)

  • Lee, Jong-Kwang;Lee, Hyo-Jik;Yoon, Kwang-Ho;Park, Byung-Suk;Yoon, Ji-Sup
    • Journal of Institute of Control, Robotics and Systems
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    • v.13 no.5
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    • pp.467-473
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    • 2007
  • In this paper, we present a position-based robot hand tracking scheme where a pan-tilt camera is controlled such that a robot hand is always shown in the center of an image frame. We calculate the rotation angles of a pan-tilt camera by transforming the coordinate systems. In order to identify the model parameters, we applied two optimization techniques: a nonlinear least square optimizer and a particle swarm optimizer. From the simulation results, it is shown that the considered parameter identification problem is characterized by a highly multimodal landscape; thus, a global optimization technique such as a particle swarm optimization could be a promising tool to identify the model parameters of a robot hand tracking system, whereas the nonlinear least square optimizer often failed to find an optimal solution even when the initial candidate solutions were selected close to the true optimum.

Hand Tracking and Hand Gesture Recognition for Human Computer Interaction

  • Bai, Yu;Park, Sang-Yun;Kim, Yun-Sik;Jeong, In-Gab;Ok, Soo-Yol;Lee, Eung-Joo
    • Journal of Korea Multimedia Society
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    • v.14 no.2
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    • pp.182-193
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    • 2011
  • The aim of this paper is to present the methodology for hand tracking and hand gesture recognition. The detected hand and gesture can be used to implement the non-contact mouse. We had developed a MP3 player using this technology controlling the computer instead of mouse. In this algorithm, we first do a pre-processing to every frame which including lighting compensation and background filtration to reducing the adverse impact on correctness of hand tracking and hand gesture recognition. Secondly, YCbCr skin-color likelihood algorithm is used to detecting the hand area. Then, we used Continuously Adaptive Mean Shift (CAMSHIFT) algorithm to tracking hand. As the formula-based region of interest is square, the hand is closer to rectangular. We have improved the formula of the search window to get a much suitable search window for hand. And then, Support Vector Machines (SVM) algorithm is used for hand gesture recognition. For training the system, we collected 1500 hand gesture pictures of 5 hand gestures. Finally we have performed extensive experiment on a Windows XP system to evaluate the efficiency of the proposed scheme. The hand tracking correct rate is 96% and the hand gestures average correct rate is 95%.

Hand Tracking based on CamShift using Motion History Image (운동 히스토리 영상을 활용한 CamShift 기반 손 추적 기법)

  • Gil, Jong In;Kim, Mina;Whang, Whankyu;Kim, Manbae
    • Journal of Broadcast Engineering
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    • v.22 no.2
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    • pp.182-192
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    • 2017
  • In this paper, we propose hand tracking system combined with color and motion information. Most of hand detection and tracking systems are performed by modeling skin color. However, in this approach, since it is highly influenced by light or surrounding objects, accurate values cannot be derived constantly. Also, depending on the skin color, hand tracking may be interrupted by not only the hand but also the background with a color similar to that of the face and skin. Therefore, we design the hand tracking that can effectively track a hand by using motion history image(MHI) and combining it with CamShift. The proposed system is implemented based on C/C++, and the experiments proved that the proposed method shows stable and excellent performance.

Development of Hand-Tracking Interaction System (핸드 트랙킹 인터랙션 시스템 개발)

  • Park, Seong-Su;Gue, Ja-Young;Hong, Jin-Ju;Rho, Young J.;Seo, Dae-Young
    • Proceedings of the Korea Information Processing Society Conference
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    • 2010.11a
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    • pp.824-826
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    • 2010
  • 컴퓨터의 활용도가 높아지면서 자연스럽게 컴퓨터에 따른 입력 장치도 사용이 잦아지고 있는 추세이다. 본 논문에서 다루고 있는 핸드 트랙킹 인터랙션 시스템(Hand-Tracking Interaction System)이란 캠에 사람의 손을 인식시켜 손의 모션에 기능을 부여하는 또 다른 차세대 입력 장치이다. 본 논문에서는 현재 사용하고 있는 마우스와 키보드 같은 입력 장치의 공간 제약성 이라는 단점을 보완하기 위해 핸드 트랙킹 인터랙션 시스템 (Hand-Tracking Interaction System) 을 개발하였고, 빛과 그림자의 영향을 쉽게 받아 손 인식률이 낮아지는 단점을 해결하기 위해 캠 대신 적외선카메라를 이용하여 인식률을 높임에 힘썼다. 또 핸드 트랙킹 인터랙션 시스템을 효율적으로 사용할 수 있는 새로운 어플리케이션을 함께 개발하였다.

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Hand Tracking and Calibration Algorithm Using the EPIC Sensors (EPIC 센서를 이용한 Hand Tracking 및 Calibration 알고리즘)

  • Jo, Jung Jae;Kim, Young Chul
    • Smart Media Journal
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    • v.2 no.1
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    • pp.27-30
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    • 2013
  • In this paper, we research the hand tracking and calibration algorithm using the EPIC sensor. We analyze the characteristics of EPIC sensor to be more sensitive in the around E-filed, and then we implement the 2-dimensional axis-transformation using the difference of detected amplitude between EPIC sensors. In addition, we implement the calibration algorithm considering the characteristics of EPIC sensor, and then we apply the Kalman filter to efficiently track a target. Thus, we implement the environment of window applications for verification and analysis the implemented algorithm. In turn, we use the DAQ API to extract the analog data. The DAQ hardware has the function of measuring and generating an electrical signal. Moreover, we confirm the movement of mouse cursor by detecting the potential difference depending on the movement of the user's hands.

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Enhanced Sign Language Transcription System via Hand Tracking and Pose Estimation

  • Kim, Jung-Ho;Kim, Najoung;Park, Hancheol;Park, Jong C.
    • Journal of Computing Science and Engineering
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    • v.10 no.3
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    • pp.95-101
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    • 2016
  • In this study, we propose a new system for constructing parallel corpora for sign languages, which are generally under-resourced in comparison to spoken languages. In order to achieve scalability and accessibility regarding data collection and corpus construction, our system utilizes deep learning-based techniques and predicts depth information to perform pose estimation on hand information obtainable from video recordings by a single RGB camera. These estimated poses are then transcribed into expressions in SignWriting. We evaluate the accuracy of hand tracking and hand pose estimation modules of our system quantitatively, using the American Sign Language Image Dataset and the American Sign Language Lexicon Video Dataset. The evaluation results show that our transcription system has a high potential to be successfully employed in constructing a sizable sign language corpus using various types of video resources.

Real-time hand tracking and recognition based on structured template matching (구조적 템플렛 매칭에 기반을 둔 실시간 손 추적 및 인식)

  • Kim, Song-Gook;Bae, Ki-Tae;Lee, Chil-Woo
    • 한국HCI학회:학술대회논문집
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    • 2006.02a
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    • pp.1037-1043
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    • 2006
  • 본 논문에서는 유비쿼터스 컴퓨팅 오피스 환경에서 가장 직관적인 HCI 수단인 손 제스처를 사용하여 대형 스크린 상의 응용 프로그램들을 쉽게 제어할 수 있는 시스템을 제안한다. 손 제스처는 손 영역의 정보, 손 중심점의 위치 변화값과 손가락 형상을 이용하여 시스템 제어에 필요한 종류들을 미리 정의해 둔다. 먼저 효율적으로 손 영역 획득을 위해 적외선 카메라를 사용하여 연속된 영상을 획득한다. 획득된 영상 프레임으로부터 구조적 템플레이트 매칭 방법을 사용하여 손의 중심(centroid) 및 손가락끝(fingertip)을 검출한다. 인식과정에서는 양손의 Euclidean distance와 손가락 형상 정보를 이용하여 미리 정의된 제스처와 비교하여 인식을 행한다. 본 논문에서 제안한 비전 기반 hand gesture 제어 시스템은 인간과 컴퓨터의 상호작용을 이해하는데 많은 이점을 제공할 수 있다. 실험 결과를 통해 본 논문에서 제안한 방법의 효율성을 입증한다.

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Human-Computer Natur al User Inter face Based on Hand Motion Detection and Tracking

  • Xu, Wenkai;Lee, Eung-Joo
    • Journal of Korea Multimedia Society
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    • v.15 no.4
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    • pp.501-507
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    • 2012
  • Human body motion is a non-verbal part for interaction or movement that can be used to involves real world and virtual world. In this paper, we explain a study on natural user interface (NUI) in human hand motion recognition using RGB color information and depth information by Kinect camera from Microsoft Corporation. To achieve the goal, hand tracking and gesture recognition have no major dependencies of the work environment, lighting or users' skin color, libraries of particular use for natural interaction and Kinect device, which serves to provide RGB images of the environment and the depth map of the scene were used. An improved Camshift tracking algorithm is used to tracking hand motion, the experimental results show out it has better performance than Camshift algorithm, and it has higher stability and accuracy as well.