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We present a Bag-of-Visual-and-Depth-Words
(BoVDW) model for gesture recognition, an extension
of the Bag-of-Visual-Words (BoVW) model, that benefits
from the multimodal fusion of visual and depth features.
State-of-the-art RGB and depth features, including
a new proposed depth descriptor, are analysed and
combined in a late fusion fashion. The method is
integrated in a continuous gesture recognition pipeline,
where Dynamic Time Warping (DTW) algorithm is
used to perform prior segmentation of gestures. Results
of the method in public data sets, within our gesture
recognition pipeline, show better performance in
comparison to a standard BoVW model.
CitationHernandez-Vela, A. [et al.]. BoVDW: Bag-of-Visual-and-Depth-Words for gesture recognition. A: International Conference on Pattern Recognition. "Proceedings of the 21st International Conference on Pattern Recognition". Tsukuba Science City: 2012, p. 449-452.
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