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titleA Decision Forest Based Feature Selection Framework for Action Recognition from RGB-Depth Cameras
authorsFarhood Negin, Firat Ozdemir, Ceyhun Burak Akgul, Kamer Ali Yuksel, Aytul Ercil
keywordshuman motion analysis, action recognition, random decision forest
abstractIn 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.
typeConference Paper
journalICIAR 2013
published year2013
serial2075
is_viewableyes
(Total records:1429)
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