Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2943
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dc.contributor.authorDas, Sonia-
dc.contributor.authorMeher, Sukadev-
dc.date.accessioned2018-03-13T10:28:09Z-
dc.date.available2018-03-13T10:28:09Z-
dc.date.issued2017-09-
dc.identifier.citationSeventh International Conference on Advances in Computing, Control and Networking (ACCN-2017), Bangkok, Thailand, 23-24 September, 2017.en_US
dc.identifier.urihttp://hdl.handle.net/2080/2943-
dc.descriptionCopyright of this document belongs to proceedings publisher.en_US
dc.description.abstractThis work presents a five-phase automatic gait recognition method that analyzes the spatiotemporal shape and dynamic motion (STS-DM) characteristics of a human subject's silhouette to identify the subject in the presence of many challenging factors that affect gait. Phase-1 describes Krawtchouk Moments for feature extraction; phase-2 describes phase weighted magnitude spectra of the Fourier descriptor of a silhouette. Phase-3 gives a full body shape and motion analysis using ellipses. In Phase-4, dynamic time warping is used to analyze thigh angle rotation pattern. In phase-5 DHT based height varying signal pattern is analyzed. Five phases are fused to give a robust identification system.en_US
dc.subjectKrawtchouk Momentsen_US
dc.subjectSTS-DMen_US
dc.subjectDTWen_US
dc.subjectDHTen_US
dc.titleAnalysis of Spatio-Temporal Dynamic Patterns of Gait for Recognitionen_US
dc.typeArticleen_US
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