short-paper

Hand posture recognition for human-robot interaction

Authors:

Tudor Ioan Cerlinca,

Stefan Gheorghe Pentiuc,

Radu Daniel Vatavu,

Marius Cristian CerlincaAuthors Info & Claims

WMISI '07: Proceedings of the 2007 workshop on Multimodal interfaces in semantic interaction

Pages 47 - 50

https://doi.org/10.1145/1330572.1330580

Published: 15 November 2007 Publication History

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Abstract

In this paper, we describe a fast and accurate method for hand posture recognition in video sequences using multiple video cameras. The technique we propose is based on the head detection, skin detection and human body proportions in order to recognize commands from real-time video sequences. Our technique is also a robust one in order to deal with changes of lighting. The experimental results show that it can be used in various vision-based applications that require real-time detection and recognition of hand posture.

References

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Paul, V., and Michael, J. 2001. Robust Real-time Object Detection. Second International Workshop on Statistical and Computational Theories of Vision - Modeling, Learning, Computing, and Sampling, Vancouver, Canada, July 13, 2001.

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Rainer, L., and Jochen, M. 2002. An Extended Set of Haarlike Features for Rapid Object Detection. IEEE ICIP 2002, Vol. 1, pp. 900--903, Sep. 2002

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Pentiuc, S. G., Vatavu, R., Cerlinca, T. I., and Ungureanu, O. 2006. Methods and Algoritms for Gestures Recognition and Understanding. The Eighth All-Ukrainian International Conference, UkrOBRAZ'2006, pp. 15--18, Ukraine, August 2006.

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http://en.wikipedia.org/wiki/HSV_color_space

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http://en.wikipedia.org/wiki/Vitruvian_Man

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Cerlinca, T. I. 2004. A Distributed System for Real Time Traffic Monitoring and Analysis. Advances in Electrical and Computer Engineering, Suceava, Romania, 2 (sept. 2004), volume 4 (11), 82--86

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Cited By

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Seib VSchmidt GKusenbach MPaulus D(2015)Fourier Features For Person Detection in Depth DataComputer Analysis of Images and Patterns10.1007/978-3-319-23192-1_69(824-836)Online publication date: 25-Aug-2015
https://doi.org/10.1007/978-3-319-23192-1_69
Heimonen THakulinen JTurunen MJokinen JKeskinen TRaisamo R(2013)Designing Gesture-Based Control for Factory AutomationHuman-Computer Interaction – INTERACT 201310.1007/978-3-642-40480-1_13(202-209)Online publication date: 2013
https://doi.org/10.1007/978-3-642-40480-1_13
Vatavu R(2011)The Understanding of Meaningful Events in Gesture-Based InteractionIntelligent Video Event Analysis and Understanding10.1007/978-3-642-17554-1_1(1-19)Online publication date: 2011
https://doi.org/10.1007/978-3-642-17554-1_1
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Reviews

Reviewer: Jason J. Corso

There is great promise in video-based recognition of natural human motion for interaction with machines and robots. However, articulated human motion presents difficult video recognition problems. Many methods, such as PFinder [1], rely on full-body tracking and pose estimation, which are computationally demanding and difficult. Other methods, such as the Everywhere Displays Project [2], fix the interaction to specific locations in the environment, and use localized image processing to solve recognition, leading to more robust but less flexible systems. This paper takes a step toward merging these two paradigms. The driving application is controlling the motion of a robot with various configurations of the operator's arms and hands. For example, both arms up tell the robot to go forward. Although contrived (why not simply use a joystick__?__), the application is representative of the rich and natural interaction with machines that is currently not possible. The authors present a sequential algorithm. First, it locates the operator's head using statistical classifier and heuristic body models. Second, it searches a set of localized regions surrounding the body for the operator's hands. Hand presence is modeled in the hue, saturation, and value (HSV) color-space, and the thresholds are learned based on the previously detected head pixels. Third, upon detecting a valid configuration of the hands, the system instructs the robot to perform the proper command. The particular choice of appearance models and head-tracking algorithms seems ad-hoc, and the results in dynamic environments are poor. For example, no dynamical model such as a Kalman filter is used to track the head. And, the authors suggest robustness to variation in illumination, but then rely on simple color thresholding to do skin detection. Nevertheless, the general approach to human-robot interaction (HRI), part-tracking and part-localized image processing, is promising. Online Computing Reviews Service

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Information & Contributors

Information

Published In

WMISI '07: Proceedings of the 2007 workshop on Multimodal interfaces in semantic interaction

November 2007

67 pages

ISBN:9781595938695

DOI:10.1145/1330572

Conference Chairs:
Naoto Iwahashi
National Institute of Information and Communications Technology, Japan
,
Mikio Nakano
Honda Research Institute Japan

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

New York, NY, United States

Publication History

Published: 15 November 2007

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ICMI07

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SIGCHI

ICMI07: International Conference on Multimodal Interface

November 15, 2007

Nagoya, Japan

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Cited By

View all

Seib VSchmidt GKusenbach MPaulus D(2015)Fourier Features For Person Detection in Depth DataComputer Analysis of Images and Patterns10.1007/978-3-319-23192-1_69(824-836)Online publication date: 25-Aug-2015
https://doi.org/10.1007/978-3-319-23192-1_69
Heimonen THakulinen JTurunen MJokinen JKeskinen TRaisamo R(2013)Designing Gesture-Based Control for Factory AutomationHuman-Computer Interaction – INTERACT 201310.1007/978-3-642-40480-1_13(202-209)Online publication date: 2013
https://doi.org/10.1007/978-3-642-40480-1_13
Vatavu R(2011)The Understanding of Meaningful Events in Gesture-Based InteractionIntelligent Video Event Analysis and Understanding10.1007/978-3-642-17554-1_1(1-19)Online publication date: 2011
https://doi.org/10.1007/978-3-642-17554-1_1
Sha LWang GYao ALin XChai X(2009)Hand posture recognition in video using multiple cuesProceedings of the 2009 IEEE international conference on Multimedia and Expo10.5555/1698924.1699141(886-889)Online publication date: 28-Jun-2009
https://dl.acm.org/doi/10.5555/1698924.1699141
Liang Sha Guijin Wang Anbang Yao Xinggang Lin Xiujuan Chai (2009)Hand posture recognition in video using multiple cues2009 IEEE International Conference on Multimedia and Expo10.1109/ICME.2009.5202637(886-889)Online publication date: Jun-2009
https://doi.org/10.1109/ICME.2009.5202637
Ghobadi SLoepprich OLottner OHartmann KWeihs WLoffeld O(2009)2D/3D Image Data Analysis for Object Tracking and ClassificationAdvances in Machine Learning and Data Analysis10.1007/978-90-481-3177-8_1(1-13)Online publication date: 30-Sep-2009
https://doi.org/10.1007/978-90-481-3177-8_1
Vatavu RGrisoni LPentiuc S(2009)Multiscale detection of gesture patterns in continuous motion trajectoriesProceedings of the 8th international conference on Gesture in Embodied Communication and Human-Computer Interaction10.1007/978-3-642-12553-9_8(85-97)Online publication date: 25-Feb-2009
https://dl.acm.org/doi/10.1007/978-3-642-12553-9_8
Ghobadi SLoepprich OAhmadov FBernshausen JHartmann KLoffeld O(2008)Real Time Hand Based Robot Control Using 2D/3D ImagesProceedings of the 4th International Symposium on Advances in Visual Computing, Part II10.1007/978-3-540-89646-3_30(307-316)Online publication date: 1-Dec-2008
https://dl.acm.org/doi/10.1007/978-3-540-89646-3_30

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