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. © 2013 Springer-Verlag.
CITATION STYLE
Negin, F., Özdemir, F., Akgül, C. B., Yüksel, K. A., & Erçil, A. (2013). A decision forest based feature selection framework for action recognition from RGB-depth cameras. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7950 LNCS, pp. 648–657). https://doi.org/10.1007/978-3-642-39094-4_74
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