Recognition of Human Periodic Movements From Unstructured Information Using A Motion-based Frequency Domain Approach

Qinggang Meng, Bo Li, Horst Holstein

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)
171 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)795-809
Number of pages15
JournalImage and Vision Computing
Volume24
Issue number8
Early online date05 Jun 2006
DOIs
Publication statusPublished - 01 Aug 2006

Keywords

  • Human periodic motion classification
  • Motion-based recognition
  • Gait analysis
  • Visual perception
  • Moving light displays (MLDs)
  • Motion power spectral analysis

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