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Recognition of human periodic movements from unstructured information using a motion-based frequency domain approach

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journal contribution
posted on 08.02.2016 by Qinggang Meng, Baihua Li, Horst Holstein
Feature-based motion cues play an important role in biological visual perception. We present a motion-based frequency-domain scheme for human periodic motion recognition. As a baseline study of feature based recognition we use unstructured feature-point kinematic data obtained directly from a marker-based optical motion capture (MoCap) system, rather than accommodate bootstrapping from the low-level image processing of feature detection. Motion power spectral analysis is applied to a set of unidentified trajectories of feature points representing whole body kinematics. Feature power vectors are extracted from motion power spectra and mapped to a low dimensionality of feature space as motion templates that offer frequency domain signatures to characterise different periodic motions. Recognition of a new instance of periodic motion against pre-stored motion templates is carried out by seeking best motion power spectral similarity. We test this method through nine examples of human periodic motion using MoCap data. The recognition results demonstrate that feature-based spectral analysis allows classification of periodic motions from low-level, un-structured interpretation without recovering underlying kinematics. Contrasting with common structure-based spatio-temporal approaches, this motion-based frequency-domain method avoids a time-consuming recovery of underlying kinematic structures in visual analysis and largely reduces the parameter domain in the presence of human motion irregularities.

History

School

  • Science

Department

  • Computer Science

Published in

IMAGE AND VISION COMPUTING

Volume

24

Issue

8

Pages

795 - 809 (15)

Citation

MENG, Q., LI, B. and HOLSTEIN, H., 2006. Recognition of human periodic movements from unstructured information using a motion-based frequency domain approach. Image and Vision Computing, 24 (8), pp.795-809

Publisher

© Elsevier Science BV

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2006

ISSN

0262-8856

Language

en

Exports