We present a probability-based DTW for gesture segmentation.We present the BoVDW framework for gesture classification.New VFHCRH descriptor for depth images. We present a methodology to address the problem of human gesture segmentation and recognition in video and depth image sequences. A Bag-of-Visual-and-Depth-Words (BoVDW) model is introduced as an extension of the Bag-of-Visual-Words (BoVW) model. State-of-the-art RGB and depth features, including a newly proposed depth descriptor, are analysed and combined in a late fusion form. The method is integrated in a Human Gesture Recognition pipeline, together with a novel probability-based Dynamic Time Warping (PDTW) algorithm which is used to perform prior segmentation of idle gestures. The proposed DTW variant uses samples of the same gesture category to build a Gaussian Mixture Model driven probabilistic model of that gesture class. Results of the whole Human Gesture Recognition pipeline in a public data set show better performance in comparison to both standard BoVW model and DTW approach.
['Antonio Hernández-Vela', 'Miguel Ángel Bautista', 'Xavier Perez-Sala', 'Víctor Ponce-López', 'Sergio Escalera', 'Xavier Baró', 'Oriol Pujol', 'Cecilio Angulo']
Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D