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Real-time Hand Tracking Using Kinect

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Lizheng LiuAuthors Info & Claims

ICDSP '18: Proceedings of the 2nd International Conference on Digital Signal Processing

Pages 37 - 42

https://doi.org/10.1145/3193025.3193056

Published: 25 February 2018 Publication History

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Abstract

Real-time hand tracking is fundamental to human gesture recognition. However, due to the huge computation, previous studies are either off-line or limited to given poses. In order to satisfy the requirement of real-time hand tracking, in this paper we propose a real time hand tracking method using Kinect. Firstly we extract the hand region from the depth image output from Kinect. Then we achieve the hand parameters. During the procedure of hand region extraction, we propose a cascade structure with recursive connected component algorithm to improve the efficiency and reserve connection relationships in 3D space. To determine the fingertips, we use the former 3D connections and geodesic distance over hand skeleton pixels to guarantee the accuracy and robustness, with acceptable loss. Experimental results show that our proposed solution can significantly improve the quality of real-time hand tracking.

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Hu ZZhang CWang XGe A(2024)Light-Adaptive Human Body Key Point Detection Algorithm Based on Multi-Source Information FusionSensors10.3390/s2410302124:10(3021)Online publication date: 10-May-2024
https://doi.org/10.3390/s24103021
Huang CLin CChen H(2024)Using autofocus data for 3D hand interactionOptical Architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality (AR, VR, MR) V10.1117/12.3000801(28)Online publication date: 12-Mar-2024
https://doi.org/10.1117/12.3000801
Patel SDeepa R(2023)Hand Gesture Recognition Used for Functioning System Using OpenCVIoT, Cloud and Data Science10.4028/p-4589o3(3-10)Online publication date: 27-Feb-2023
https://doi.org/10.4028/p-4589o3
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Index Terms

Real-time Hand Tracking Using Kinect
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Natural language interfaces

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ICDSP '18: Proceedings of the 2nd International Conference on Digital Signal Processing

February 2018

198 pages

ISBN:9781450364027

DOI:10.1145/3193025

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 25 February 2018

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Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications, Ministry of Education, JZNY201704)
Nanjing University of Posts and Telecommunications (NY217021)

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ICDSP 2018

ICDSP 2018: 2018 2nd International Conference on Digital Signal Processing

February 25 - 27, 2018

Tokyo, Japan

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Hu ZZhang CWang XGe A(2024)Light-Adaptive Human Body Key Point Detection Algorithm Based on Multi-Source Information FusionSensors10.3390/s2410302124:10(3021)Online publication date: 10-May-2024
https://doi.org/10.3390/s24103021
Huang CLin CChen H(2024)Using autofocus data for 3D hand interactionOptical Architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality (AR, VR, MR) V10.1117/12.3000801(28)Online publication date: 12-Mar-2024
https://doi.org/10.1117/12.3000801
Patel SDeepa R(2023)Hand Gesture Recognition Used for Functioning System Using OpenCVIoT, Cloud and Data Science10.4028/p-4589o3(3-10)Online publication date: 27-Feb-2023
https://doi.org/10.4028/p-4589o3
Mujahid AAslam MKhan MMartinez-Enriquez AHaq N(2023)Multi-Class Confidence Detection Using Deep Learning ApproachApplied Sciences10.3390/app1309556713:9(5567)Online publication date: 30-Apr-2023
https://doi.org/10.3390/app13095567
Fadel NKareem E(2023)Computer Vision Techniques for Hand Gesture Recognition: SurveyNew Trends in Information and Communications Technology Applications10.1007/978-3-031-35442-7_4(50-76)Online publication date: 11-Jun-2023
https://doi.org/10.1007/978-3-031-35442-7_4
Batmaz AStuerzlinger W(2023)Rotational and Positional Jitter in Virtual Reality Interaction in Everyday VREveryday Virtual and Augmented Reality10.1007/978-3-031-05804-2_4(89-118)Online publication date: 19-Feb-2023
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Moysiadis VKatikaridis DBenos LBusato PAnagnostis AKateris DPearson SBochtis D(2022)An Integrated Real-Time Hand Gesture Recognition Framework for Human–Robot Interaction in AgricultureApplied Sciences10.3390/app1216816012:16(8160)Online publication date: 15-Aug-2022
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Marzuki HHashim N(2022)Dynamic Hand Gesture Recognition Based on Deep Learning for Muslim Elderly CareProceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)10.2991/978-94-6463-094-7_44(544-557)Online publication date: 25-Dec-2022
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Asthana OBhakuni PSrivastava PSingh SJindal K(2022)Sign Language Recognition Based on Gesture Recognition/Holistic Features Recognition: A Review of Techniques2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)10.1109/ICIPTM54933.2022.9754140(713-718)Online publication date: 23-Feb-2022
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Khalaf LAswad SAhmed SMakki BAhmed M(2022)Survey On Recognition Hand Gesture By Using Data Mining Algorithms2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)10.1109/HORA55278.2022.9800090(1-4)Online publication date: 9-Jun-2022
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Multi-sensor data fusion for hand tracking using Kinect and Leap Motion

A Real-Time Hand Posture Recognition System Using Deep Neural Networks

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Multi-sensor data fusion for hand tracking using Kinect and Leap Motion

A Real-Time Hand Posture Recognition System Using Deep Neural Networks

A real time vision-based hand gestures recognition system

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