Body gesture classification based on Bag-of-features in frequency domain of motion

In this paper, we propose a method for semantic motion retrieval in large data sets of human motions to classify body gestures automatically. This method extracts spatio-temporal features from the motions by expressing them in frequency domain. And these features are transformed into the Bag-of-words representation to accelerate the calculation and to emphasize the semantic aspect. The method is inspired by techniques of natural language processing or image processing. We conducted experiments for evaluating the performance of the motion classification using data sets captured by a motion capture system. Through the experiments, we confirmed that our method improves the performance of the motion classification and reduces the computational time drastically.