Show simple item record Meng, Qinggang Li, Bo Holstein, Horst 2008-12-17T10:57:53Z 2008-12-17T10:57:53Z 2006-08-01
dc.identifier.citation Meng , Q , Li , B & Holstein , H 2006 , ' Recognition of Human Periodic Movements From Unstructured Information Using A Motion-based Frequency Domain Approach ' Image and Vision Computing , vol 24 , no. 8 , pp. 795-809 . DOI: 10.1016/j.imavis.2006.01.033 en
dc.identifier.issn 0262-8856
dc.identifier.other PURE: 97244
dc.identifier.other PURE UUID: 09e6665c-0b0f-4b70-85f7-cfc8dfb10373
dc.identifier.other dspace: 2160/1745
dc.identifier.other DSpace_20121128.csv: row: 1452
dc.identifier.other Scopus: 33746905570
dc.description Holstein, Horst, Meng, Q., Li, B., (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 RAE2008 en
dc.description.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. en
dc.format.extent 15 en
dc.language.iso eng
dc.relation.ispartof Image and Vision Computing en
dc.rights en
dc.subject Human periodic motion classification en
dc.subject Motion-based recognition en
dc.subject Gait analysis en
dc.subject Visual perception en
dc.subject Moving light displays (MLDs) en
dc.subject Motion power spectral analysis en
dc.title Recognition of Human Periodic Movements From Unstructured Information Using A Motion-based Frequency Domain Approach en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.contributor.institution Intelligent Robotics Group en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Department of Physics en
dc.description.status Peer reviewed en

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