• Title, Summary, Keyword: hand tracking

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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%.

Robust 3D Hand Tracking based on a Coupled Particle Filter (결합된 파티클 필터에 기반한 강인한 3차원 손 추적)

  • Ahn, Woo-Seok;Suk, Heung-Il;Lee, Seong-Whan
    • Journal of KIISE:Software and Applications
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    • v.37 no.1
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    • pp.80-84
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    • 2010
  • Tracking hands is an essential technique for hand gesture recognition which is an efficient way in Human Computer Interaction (HCI). Recently, many researchers have focused on hands tracking using a 3D hand model and showed robust tracking results compared to using 2D hand models. In this paper, we propose a novel 3D hand tracking method based on a coupled particle filter. This provides robust and fast tracking results by estimating each part of global hand poses and local finger motions separately and then utilizing the estimated results as a prior for each other. Furthermore, in order to improve the robustness, we apply a multi-cue based method by integrating a color-based area matching method and an edge-based distance matching method. In our experiments, the proposed method showed robust tracking results for complex hand motions in a cluttered background.

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.

Controlling Slides using Hand tracking and Gesture Recognition (손의 추적과 제스쳐 인식에 의한 슬라이드 제어)

  • Fayyaz, Rabia;Rhee, Eun Joo
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.436-439
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    • 2012
  • The work is to the control the desktop Computers based on hand gesture recognition. This paper is worked en real time tracking and recognizes the hand gesture for controlling the slides based on hand direction such as right and left using a real time camera.

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Face and Hand Tracking Algorithm for Sign Language Recognition (수화 인식을 위한 얼굴과 손 추적 알고리즘)

  • Park, Ho-Sik;Bae, Cheol-Soo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.31 no.11C
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    • pp.1071-1076
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    • 2006
  • In this paper, we develop face and hand tracking for sign language recognition system. The system is divided into two stages; the initial and tracking stages. In initial stage, we use the skin feature to localize face and hands of signer. The ellipse model on CbCr space is constructed and used to detect skin color. After the skin regions have been segmented, face and hand blobs are defined by using size and facial feature with the assumption that the movement of face is less than that of hands in this signing scenario. In tracking stage, the motion estimation is applied only hand blobs, in which first and second derivative are used to compute the position of prediction of hands. We observed that there are errors in the value of tracking position between two consecutive frames in which velocity has changed abruptly. To improve the tracking performance, our proposed algorithm compensates the error of tracking position by using adaptive search area to re-compute the hand blobs. The experimental results indicate that our proposed method is able to decrease the prediction error up to 96.87% with negligible increase in computational complexity of up to 4%.

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.

Design of Computer Vision Interface by Recognizing Hand Motion (손동작 인식에 의한 컴퓨터 비전 인터페이스 설계)

  • Yun, Jin-Hyun;Lee, Chong-Ho
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.47 no.3
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    • pp.1-10
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    • 2010
  • As various interfacing devices for computational machines are being developed, a new HCI method using hand motion input is introduced. This interface method is a vision-based approach using a single camera for detecting and tracking hand movements. In the previous researches, only a skin color is used for detecting and tracking hand location. However, in our design, skin color and shape information are collectively considered. Consequently, detection ability of a hand increased. we proposed primary orientation edge descriptor for getting an edge information. This method uses only one hand model. Therefore, we do not need training processing time. This system consists of a detecting part and a tracking part for efficient processing. In tracking part, the system is quite robust on the orientation of the hand. The system is applied to recognize a hand written number in script style using DNAC algorithm. Performance of the proposed algorithm reaches 82% recognition ratio in detecting hand region and 90% in recognizing a written number in script style.

RGB Camera-based Real-time 21 DoF Hand Pose Tracking (RGB 카메라 기반 실시간 21 DoF 손 추적)

  • Choi, Junyeong;Park, Jong-Il
    • Journal of Broadcast Engineering
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    • v.19 no.6
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    • pp.942-956
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    • 2014
  • This paper proposes a real-time hand pose tracking method using a monocular RGB camera. Hand tracking has high ambiguity since a hand has a number of degrees of freedom. Thus, to reduce the ambiguity the proposed method adopts the step-by-step estimation scheme: a palm pose estimation, a finger yaw motion estimation, and a finger pitch motion estimation, which are performed in consecutive order. Assuming a hand to be a plane, the proposed method utilizes a planar hand model, which facilitates a hand model regeneration. The hand model regeneration modifies the hand model to fit a current user's hand, and improves robustness and accuracy of the tracking results. The proposed method can work in real-time and does not require GPU-based processing. Thus, it can be applied to various platforms including mobile devices such as Google Glass. The effectiveness and performance of the proposed method will be verified through various experiments.

An Improved Approach for 3D Hand Pose Estimation Based on a Single Depth Image and Haar Random Forest

  • Kim, Wonggi;Chun, Junchul
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.8
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    • pp.3136-3150
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    • 2015
  • A vision-based 3D tracking of articulated human hand is one of the major issues in the applications of human computer interactions and understanding the control of robot hand. This paper presents an improved approach for tracking and recovering the 3D position and orientation of a human hand using the Kinect sensor. The basic idea of the proposed method is to solve an optimization problem that minimizes the discrepancy in 3D shape between an actual hand observed by Kinect and a hypothesized 3D hand model. Since each of the 3D hand pose has 23 degrees of freedom, the hand articulation tracking needs computational excessive burden in minimizing the 3D shape discrepancy between an observed hand and a 3D hand model. For this, we first created a 3D hand model which represents the hand with 17 different parts. Secondly, Random Forest classifier was trained on the synthetic depth images generated by animating the developed 3D hand model, which was then used for Haar-like feature-based classification rather than performing per-pixel classification. Classification results were used for estimating the joint positions for the hand skeleton. Through the experiment, we were able to prove that the proposed method showed improvement rates in hand part recognition and a performance of 20-30 fps. The results confirmed its practical use in classifying hand area and successfully tracked and recovered the 3D hand pose in a real time fashion.

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.