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A Decision Forest Based Feature Selection Framework for Action Recognition from RGB-Depth Cameras
Authors: Farhood Negin, Firat Ozdemir, Ceyhun Burak Akgul, Kamer Ali Yuksel, Aytul Ercil
Published in: ICIAR 2013
Publication year: 2013
Abstract: In this paper, we present an action recognition framework
leveraging data mining capabilities of random decision forests trained on
kinematic features. We describe human motion via a rich collection of
kinematic feature time-series computed from the skeletal representation
of the body in motion. We discriminatively optimize a random decision
forest model over this collection to identify the most effective subset
of features, localized both in time and space. Later, we train a support
vector machine classifier on the selected features. This approach improves
upon the baseline performance obtained using the whole feature set with
a significantly less number of features (one tenth of the original). On
MSRC-12 dataset (12 classes), our method achieves 94% accuracy. On
the WorkoutSU-10 dataset, collected by our group (10 physical exercise
classes), the accuracy is 98%. The approach can also be used to provide
insights on the spatiotemporal dynamics of human actions.
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