We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Event-driven body motion analysis for real-time gesture recognition

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Kohn, B. ; AIT Austrian Inst. of Technol. GmbH, Vienna, Austria ; Belbachir, A.N. ; Hahn, T. ; Kaufmann, Hannes

This paper presents an evaluation of spatio-temporal data generated by a dynamic stereo vision sensor in a highdimensional space (3D volume and time) for motion analysis and gesture recognition. In contrast to traditional frame-based (synchronous) stereo cameras, dynamic stereo vision sensors asynchronously generates events upon scene dynamics. Motion activities are intrinsically (on-chip) segmented by the sensor, such that activity, gesture recognition and tracking can be intuitively and efficiently performed. In this work, we investigated the applicability of this sensor for gesture recognition. We developed a machine learning method based on the Hidden Markow Model for training and automated classifications of gestures using the event data generated by the sensor. By training eight different activities (dance figures) with 15 persons we build up a library of 580 recorded activities. An average recognition rate of 97% has been reached.

Published in:

Circuits and Systems (ISCAS), 2012 IEEE International Symposium on

Date of Conference:

20-23 May 2012