Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/2943
Title: | Analysis of Spatio-Temporal Dynamic Patterns of Gait for Recognition |
Authors: | Das, Sonia Meher, Sukadev |
Keywords: | Krawtchouk Moments STS-DM DTW DHT |
Issue Date: | Sep-2017 |
Citation: | Seventh International Conference on Advances in Computing, Control and Networking (ACCN-2017), Bangkok, Thailand, 23-24 September, 2017. |
Abstract: | This 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. |
Description: | Copyright of this document belongs to proceedings publisher. |
URI: | http://hdl.handle.net/2080/2943 |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2017_ACCN_SMeher_Analysis.pdf | Conference Paper | 437.07 kB | Adobe PDF | View/Open |
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