research-article

Real-time Hand Tracking Using Kinect

Authors:

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

Get Access

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.

References

[1]

Sridhar S., Mueller F., Oulasvirta A., et al. Fast and robust hand tracking using detection-guided optimization{C}//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 3213--3221.

Google Scholar

[2]

Chaudhary A., Raheja J. L., Das K., et al. Intelligent approaches to interact with machines using hand gesture recognition in natural way: a survey{J}. arXiv preprint arXiv:1303. 2292--2013.

Google Scholar

[3]

Lun R., Zhao W. A survey of applications and human motion recognition with microsoft kinect{J}. International Journal of Pattern Recognition and Artificial Intelligence, 2015, 29(05): 1555008.

Crossref

Google Scholar

[4]

Taylor J., Bordeaux L., Cashman T., et al. Efficient and precise interactive hand tracking through joint, continuous optimization of pose and correspondences{J}. ACM Transactions on Graphics (TOG), 2016, 35(4): 143.

Digital Library

Google Scholar

[5]

Han J., Shao L., Xu D., et al. Enhanced computer vision with microsoft kinect sensor: A review{J}. IEEE transactions on cybernetics, 2013, 43(5): 1318--1334.

Google Scholar

[6]

Wu Y., Huang T. S. Vision-based gesture recognition: A review{C}//International Gesture Workshop. Springer Berlin Heidelberg, 1999: 103--115.

Digital Library

Google Scholar

[7]

Yoo K. Y. Robust hand segmentation and tracking to illumination variation{C}//Consumer Electronics (ICCE), 2014 IEEE International Conference on. IEEE, 2014: 286--287.

Google Scholar

[8]

Tara R., Santosa P., Adji T. Hand segmentation from depth image using anthropometric approach in natural interface development{J}. Int. J. Sci. Eng. Res, 2012, 3(5): 1--4.

Google Scholar

[9]

Hackenberg G., McCall R., Broll W. Lightweight palm and finger tracking for real-time 3D gesture control{C}//Virtual Reality Conference (VR), 2011 IEEE. IEEE, 2011: 19--26.

Digital Library

Google Scholar

[10]

Liang H., Yuan J., Thalmann D. 3D fingertip and palm tracking in depth image sequences{C}//Proceedings of the 20th ACM international conference on Multimedia. ACM, 2012: 785--788.

Digital Library

Google Scholar

[11]

Lee U., Tanaka J. Hand controller: Image manipulation interface using fingertips and palm tracking with Kinect depth data{C}//Proc. Asia Pacific Conf. Comput. Human Interact. 2012: 705--706.

Google Scholar

[12]

She Y., Wang Q., Jia Y., et al. A real-time hand gesture recognition approach based on motion features of feature points{C}//Computational Science and Engineering (CSE), 2014 IEEE 17th International Conference on. IEEE, 2014: 1096--1102.

Digital Library

Google Scholar

[13]

Krejov P., Bowden R. Multi-touchless: Real-time fingertip detection and tracking using geodesic maxima{C}//Automatic Face and Gesture Recognition (FG), 2013 10th IEEE International Conference and Workshops on. IEEE, 2013: 1--7.

Google Scholar

Cited By

View all

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
Show More Cited By

Index Terms

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

Recommendations

Multi-sensor data fusion for hand tracking using Kinect and Leap Motion
VRIC '14: Proceedings of the 2014 Virtual Reality International Conference

Often presented as competing products on the market of low cost 3D sensors, the Kinect™ and the Leap Motion™ (LM) can actually be complementary in some scenario. We promote, in this paper, the fusion of data acquired by both LM and Kinect sensors to ...
A Real-Time Hand Posture Recognition System Using Deep Neural Networks
Special Section on Visual Understanding with RGB-D Sensors

Hand posture recognition (HPR) is quite a challenging task, due to both the difficulty in detecting and tracking hands with normal cameras and the limitations of traditional manually selected features. In this article, we propose a two-stage HPR system ...
A real time vision-based hand gestures recognition system
ISICA'10: Proceedings of the 5th international conference on Advances in computation and intelligence

Hand gesture recognition is an important aspect in Human-Computer interaction, and can be used in various applications, such as virtual reality and computer games. In this paper, we propose a real time hand gesture recognition system. It includes three ...

Comments

Information & Contributors

Information

Published In

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 February 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

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)

Conference

ICDSP 2018

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

February 25 - 27, 2018

Tokyo, Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
389
Total Downloads

Downloads (Last 12 months)29
Downloads (Last 6 weeks)2

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

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
https://doi.org/10.1007/978-3-031-05804-2_4
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
https://doi.org/10.3390/app12168160
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
https://doi.org/10.2991/978-94-6463-094-7_44
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
https://doi.org/10.1109/ICIPTM54933.2022.9754140
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
https://doi.org/10.1109/HORA55278.2022.9800090
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

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

Comments

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

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

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Get Access

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations