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1 Journal of Theoretical and Applied Computer Science

1 2014

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An RDF-based action recognition framework with feature selection capability, considering therapy exercises utilizing depth cameras

Negin F., Akgül C.B., Yüksel K.A., Erçil A.

Journal of Theoretical and Applied Computer Science

2014

Vol. 8, nr 3

3--22

Emerging cost-efficient depth sensor technologies reveal new possibilities to cope with difficulties in action recognition. Depth information improves the quality of skeleton detection process, hence, pose estimation can be done more efficiently. Recently many studies focus on temporal analyses over estimated skeleton poses to recognize actions. In this paper we have an inclusive study of the spatiotemporal kinematic features and propose an action recognition framework with feature selection capability to deal with the multitudinous of features by leveraging data mining capabilities of random decision forests. 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). To justify our method we test the framework on various datasets and compared it with state-of-theart. On MSRC-12 dataset [25] (12 classes), our method achieves 94% accuracy. On the WorkoutSU-10 dataset [28], collected by our group (10 physical exercise classes), the accuracy is 98%. On MSR Action3D dataset [9] (20 classes) we obtain 87% average accuracy and for UTKinect-Action dataset [10] (10 classes) the accuracy is 92%. Other than regular activities, we also tried our approach to detect a falling person using the dataset which we recorded as an extension to our original dataset. We test how our method adjusts on different types of actions and we obtained promising results for this type of action. We discuss that our approach provides insights on the spatiotemporal dynamics of human actions and can be used to as part of different applications especially for rehabilitation of patients.