Please use this identifier to cite or link to this item:
Title: Mutual Information of Features Extracted from Human Micro-Doppler
Authors: Tekeli, Bürkan
Gürbüz, Sevgi Zübeyde
Yüksel, Melda
Keywords: human classification
feature extraction
information theory I
Issue Date: 2013
Publisher: IEEE
Source: 21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUS
Series/Report no.: Signal Processing and Communications Applications Conference
Abstract: The unique, bi-pedal motion of humans has been shown to generate a characteristic micro-Doppler signature in the time-frequency domain that can be used to discriminate humans from not just other targets, but also between different activities, such as walking and running. In the literature, many different features have been proposed for classification applications. However, it is not known which features have a greater impact on classification performance, or indeed how many features should be used to achieve good classification. In this work, the mutual information of features extracted from human micro-Doppler signatures is computed. Taking the problem of classifying human arm-swing as an example, the features extracted are ordered in terms of importance.
ISBN: 978-1-4673-5563-6; 978-1-4673-5562-9
ISSN: 2165-0608
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender

Page view(s)

checked on Dec 26, 2022

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.