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)
Official URL: http://dx.doi.org/10.1007/978-3-319-93000-8_42
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%.
Repository Staff Only: item control page