Human action recognition using fusion of depth and inertial sensors

Fuad, Zain and Ünel, Mustafa (2018) Human action recognition using fusion of depth and inertial sensors. In: Campilho, Aurelio and Karray, Fakhri and Ter Haar Romeny, Bart, (eds.) Image Analysis and Recognition. Lecture Notes in Computer Science, 10882. Springer, Switzerland, pp. 373-380. ISBN 978-3-319-92999-6 (Print) 978-3-319-93000-8 (Online)

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Abstract

In this paper we present a human action recognition system that utilizes the fusion of depth and inertial sensor measurements. Robust depth and inertial signal features, that are subject-invariant, are used to train independent Neural Networks, and later decision level fusion is employed using a probabilistic framework in the form of Logarithmic Opinion Pool. The system is evaluated using UTD-Multimodal Human Action Dataset, and we achieve 95% accuracy in 8-fold cross-validation, which is not only higher than using each sensor separately, but is also better than the best accuracy obtained on the mentioned dataset by 3.5%.
Item Type: Book Section / Chapter
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7885-7895 Computer engineering. Computer hardware
T Technology > TJ Mechanical engineering and machinery > TJ163.12 Mechatronics
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Mechatronics
Faculty of Engineering and Natural Sciences
Depositing User: Mustafa Ünel
Date Deposited: 12 Aug 2018 21:08
Last Modified: 26 Apr 2022 08:36
URI: https://research.sabanciuniv.edu/id/eprint/35817

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