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2014-09-06

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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/119123

Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D

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Recurs relacionat

https://doi.org/10.1016/j.patrec.2013.09.009

Resum

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.

Matèries

Algorismes computacionals, Processos gaussians

Matèries (anglès)

Computer algorithms, Gaussian processes

Citació

Col·leccions

Articles publicats en revistes (Matemàtiques i Informàtica)

Citació

HERNÁNDEZ-VELA, Antonio, et al. Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D. Pattern Recognition Letters. 2014. Vol. 50, num. 112-121. ISSN 0167-8655. [consulted: 16 of August of 2026]. Available at: https://hdl.handle.net/2445/119123

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