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Hand detection on sign language videos

Authors:

Christopher Conly,

Vassilis AthitsosAuthors Info & Claims

PETRA '14: Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments

Article No.: 26, Pages 1 - 5

https://doi.org/10.1145/2674396.2674442

Published: 27 May 2014 Publication History

Abstract

For gesture and sign language recognition, hand shape and hand motion are the primary sources of information that differentiate one sign from another. Building an efficient and reliable hand detector is therefore an important step in recognizing signs and gestures. In this paper we evaluate three hand detection methods on three sign language data sets: a skin and motion detector [1], hand detection using multiple proposals [12], and chains model [9].

References

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Digital Library

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V. Athitsos, J. Wang, S. Sclaroff, and M. Betke. Detecting instances of shape classes that exhibit variable structure. In European Conference on Computer Vision, pages 121--134, 2006.

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P. Buehler, M. Everingham, D. P. Huttenlocher, and A. Zisserman. Long term arm and hand tracking for continuous sign language TV broadcasts. In British Machine Vision Conference, 2008.

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T. Pfister, J. Charles, M. Everingham, and A. Zisserman. Automatic and efficient long term arm and hand tracking for continuous sign language tv broadcasts. In British Machine Vision Conference, 2012.

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Cited By

Beugher SBrône GGoedemé T(2018)A semi-automatic annotation tool for unobtrusive gesture analysisLanguage Resources and Evaluation10.1007/s10579-017-9404-952:2(433-460)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s10579-017-9404-9
Mei KXu LLi BLin BWang F(2015)A real-time hand detection system based on multi-featureNeurocomputing10.1016/j.neucom.2015.01.049158(184-193)Online publication date: Jun-2015
https://doi.org/10.1016/j.neucom.2015.01.049

Index Terms

Hand detection on sign language videos
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Touch screens

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Information & Contributors

Information

Published In

cover image ACM Other conferences

PETRA '14: Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments

May 2014

408 pages

ISBN:9781450327466

DOI:10.1145/2674396

Conference Chair:
Fillia Makedon
University of Texas at Arlington
,
Program Chairs:
Mark Clements
Georgia Institute of Technology
,
Catherine Pelachaud
TELECOM ParisTech, France
,
Vana Kalogeraki
Athens University of Economics and Bus
,
Ilias Maglogiannis
University of Piraeus, Greece

Copyright © 2014 ACM.

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 the author(s) 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].

Sponsors

iPerform Center: iPerform Center for Assistive Technologies to Enhance Human Performance
CSE@UTA: Department of Computer Science and Engineering, The University of Texas at Arlington
HERACLEIA: HERACLEIA Human-Centered Computing Laboratory at UTA
U of Tex at Arlington: U of Tex at Arlington
NCRS: Demokritos National Center for Scientific Research
Fulbrigh, Greece: Fulbright Foundation, Greece

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2014

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hand detection

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Research-article

Funding Sources

National Science Foundation

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PETRA '14

Sponsor:

iPerform Center
CSE@UTA
HERACLEIA
U of Tex at Arlington
NCRS
Fulbrigh, Greece

PETRA '14: The 7th International Conference on PErvasive Technologies Related to Assistive Environments

May 27 - 30, 2014

Rhodes, Greece

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Cited By

Beugher SBrône GGoedemé T(2018)A semi-automatic annotation tool for unobtrusive gesture analysisLanguage Resources and Evaluation10.1007/s10579-017-9404-952:2(433-460)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s10579-017-9404-9
Mei KXu LLi BLin BWang F(2015)A real-time hand detection system based on multi-featureNeurocomputing10.1016/j.neucom.2015.01.049158(184-193)Online publication date: Jun-2015
https://doi.org/10.1016/j.neucom.2015.01.049

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