A decision forest based feature selection framework for action recognition from RGB-Depth cameras
Negin, Farhood and Özdemir, Fırat and Yüksel, Kamer Ali and Akgül, Ceyhun Burak and Erçil, Aytül (2013) A decision forest based feature selection framework for action recognition from RGB-Depth cameras. In: 21st Signal Processing and Communications Applications Conference (SIU 2013), Haspolat, Cyprus
Official URL: http://dx.doi.org/10.1109/SIU.2013.6531398
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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