BoVDW: Bag-of-Visual-and-Depth-Words for gesture recognition

By Antonio Hernández-Vela, Miguel Angel Bautista, Xavier Perez-Sala, Víctor Ponce-López, Xavier Baró, Oriol Pujol, Cecilio Angulo, Sergio Escalera. 2012

41 citations recorded by the source corpus.

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Citations in the corpus

One-shot-learning gesture recognition using HOG-HOF features (2013, methodology): “Hernández-Vela et al. (2012) present a Bag-of-Visual-and-Depth-Words (BoVDW) model for gesture recognition, that benefits from the multimodal fusion of visual and depth features.”
Survey on 3D Hand Gesture Recognition (2016, methodology): “[154] extended the Bag of Words [155] model to bag-of-visualand-depth-words model for gesture recognition.”
One-shot learning gesture recognition from RGB-D data using bag of features (2013, methodology): “Here, we select BoF-based model to recognize gestures because it reveals promising results for one-shot learning (Hernández-Vela et al., 2012) and has a number of attractive properties.”
Using Convolutional 3D Neural Networks for User-independent continuous gesture recognition (2016, methodology): “proposed an adaptation of Bag of Words methods to recognize hand gestures, termed a Bag-of-Visualand-Depth-Words (BoVDW) [9].”
3D SMoSIFT: three-dimensional sparse motion scale invariant feature transform for activity recognition from RGB-D videos (2014, methodology): “Hernández-Vela et al.11 used Harris3D detector16 to detect keypoints on RGB and depth sequences, respectively.”
Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera (2013, methodology): “[7] uses VFHCRH to describe the 2D depth image patch around the STIPs, Zhao et al.”
Efficient Multi-cue Scene Segmentation (2013, background): “Furthermore, features obtained from dense depth maps have also been quantized in the context of gesture recognition [14].”