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Selection of individual gait features extracted by MPCA applied to video recordings data

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)

Abstract

The scope of this article is selection of individual gait features of video recordings data. The gait sequences are considered to be the 3rd-order tensors and their features are extracted by Multilinear Principal Component Analysis. Obtained gait descriptors are reduced by the supervised selection with greedy hill climbing and genetics search methods. To evaluate the explored individual feature sets, classification is carried out and CFS correlation based measure is utilized. The experimental phase is based on the CASIA Gait Database 'dataset A'. The obtained results are promising. Feature selection gives much more compact gait descriptors and causes significant improvement of human identification

Original languageEnglish
Title of host publicationVision Based Systems for UAV Applications
EditorsAleksander Nawrat, Aleksander Nawrat, Zygmunt Kus
Pages257-271
Number of pages15
DOIs
Publication statusPublished - 2013

Publication series

NameStudies in Computational Intelligence
Volume481
ISSN (Print)1860-949X

ASJC Scopus subject areas

  • Artificial Intelligence

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