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Quasi View-Independent Human Motion Recognition in Subspaces

We demonstrate the efficiency of a proposed feature extraction technique for dimensionality reduction in classifying human motions using MoCap data. We follow a technique introduced by Körner and Denzler [1] for designing feature vectors before and after a transformation of these features into a subspace. This approach uses an average of a skeleton in a video in combination with a specific normalization to form an action descriptor. The major advantage of this approach is that it yields a very small fixed data size regardless of video length. This leads to a very fast computation with any classifiers. In our experiment, we show the robustness of applying this feature extraction technique for various dimensionality reduction approaches under two conditions: i) untrained camera angles in combination with untrained subjects, and ii) data loss; by further extension of i) by subsampling the original test data. Furthermore, we suggest an anatomically in-frame normalization which can improve the classification performance in several scenarios.