Event-driven Body Motion Analysis For Real-time Gesture Recognition

B. Kohn, A. Belbachir, T Hahn, H. Kaufmann:
"Event-driven Body Motion Analysis For Real-time Gesture Recognition";
in:"Proceedings of the IEEE International Symposium on Circuits and Systems", IEEE Computer Society, 2012, S. 703 - 706.

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Abstract:


This paper presents an evaluation of spatiotemporal
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 lerning 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 activites. An average recognition rate of 97%
has been reached.