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Sensors 2013, 13(7), 8835-8855; doi:10.3390/s130708835

Model-Based Reinforcement of Kinect Depth Data for Human Motion Capture Applications

1
Polythecnic School of Cáceres, University of Extremadura, Avd. de la Universidad, Cáceres 10003,Spain
2
Department of Electronic Technology, University of Málaga, Campus de Teatinos, Málaga 29071, Spain
3
Biomechanics of Human Movement and Ergonomics Lab, BioẼrgon Research Group, University of Extremadura, Avd. de la Universidad, Cáceres 10003, Spain
*
Author to whom correspondence should be addressed.
Received: 15 May 2013 / Revised: 2 July 2013 / Accepted: 8 July 2013 / Published: 10 July 2013
(This article belongs to the Section Physical Sensors)
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Abstract

Motion capture systems have recently experienced a strong evolution. New cheap depth sensors and open source frameworks, such as OpenNI, allow for perceiving human motion on-line without using invasive systems. However, these proposals do not evaluate the validity of the obtained poses. This paper addresses this issue using a model-based pose generator to complement the OpenNI human tracker. The proposed system enforces kinematics constraints, eliminates odd poses and filters sensor noise, while learning the real dimensions of the performer’s body. The system is composed by a PrimeSense sensor, an OpenNI tracker and a kinematics-based filter and has been extensively tested. Experiments show that the proposed system improves pure OpenNI results at a very low computational cost. View Full-Text
Keywords: human motion capture; sensor; RGB-D sensors; range camera; pose analysis human motion capture; sensor; RGB-D sensors; range camera; pose analysis
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Calderita, L.V.; Bandera, J.P.; Bustos, P.; Skiadopoulos, A. Model-Based Reinforcement of Kinect Depth Data for Human Motion Capture Applications. Sensors 2013, 13, 8835-8855.

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