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- W72718568 abstract "Dynamic Time Warping DTW is commonly used in gesture recognition tasks in order to tackle the temporal length variability of gestures. In the DTW framework, a set of gesture patterns are compared one by one to a maybe infinite test sequence, and a query gesture category is recognized if a warping cost below a certain threshold is found within the test sequence. Nevertheless, either taking one single sample per gesture category or a set of isolated samples may not encode the variability of such gesture category. In this paper, a probability-based DTW for gesture recognition is proposed. Different samples of the same gesture pattern obtained from RGB-Depth data are used to build a Gaussian-based probabilistic model of the gesture. Finally, the cost of DTW has been adapted accordingly to the new model. The proposed approach is tested in a challenging scenario, showing better performance of the probability-based DTW in comparison to state-of-the-art approaches for gesture recognition on RGB-D data." @default.
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- W72718568 date "2013-01-01" @default.
- W72718568 modified "2023-10-16" @default.
- W72718568 title "Probability-Based Dynamic Time Warping for Gesture Recognition on RGB-D Data" @default.
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- W72718568 doi "https://doi.org/10.1007/978-3-642-40303-3_14" @default.
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