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Rotation and scale invariant posture recognition using Microsoft Kinect skeletal tracking feature

conference contribution
posted on 01.12.2012, 00:00 authored by Samiul Monir, Sabirat Rubya, Hasan FerdousHasan Ferdous
Human posture identification for motion controlling applications is becoming more of a challenge. We present a posture classification system using skeletal-tracking feature of Microsoft Kinect sensor. Posture recovery is carried out by detecting the human body joints, its position, and orientation at the same time. Angular representation of the skeleton data makes the system very robust and avoids problems related to human body occlusions and motion ambiguities. The implemented system is tested on a class of relatively common postures comprising hundreds of human pose instances by different people, where our classifier shows an average accuracy of 94.9%, 96.7% and 96.9% for linear, exponential and priority based matching systems respectively.

History

Event

ISDA - Intelligent Systems Design and Applications. International Conference (12th : 2012 : Kochi, India)

Pagination

404 - 409

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Location

Kochi, India

Place of publication

Piscataway, N.J.

Start date

27/11/2012

End date

29/11/2012

ISSN

2164-7143

eISSN

2164-7151

ISBN-13

9781467351188

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2012, IEEE

Title of proceedings

ISDA 2012 : Proceedings of the 12th International Conference on Intelligent Systems Design and Applications