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Silhouette-based human action recognition by embedding HOG and PCA features
Published in Springer
Volume: 673
Pages: 363 - 371
Human action recognition has become vital aspect of video analytics. This study explores methods for the classification of human actions by extracting silhouette of object and then applying feature extraction. The method proposes to integrate HOG feature and PCA feature effectively to form a feature descriptor which is used further to train KNN classifier. HOG gives local shape-oriented variations of the object while PCA gives global information about frequently moving parts of human body. Experiments conducted on Weizmann and KTH datasets show results comparable with existing methods. © 2018, Springer Nature Singapore Pte Ltd.
About the journal
JournalData powered by TypesetAdvances in Intelligent Systems and Computing
PublisherData powered by TypesetSpringer
Open AccessNo