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HANDS: an RGB-D dataset of static hand-gestures for human-robot interaction

Articolo
Data di Pubblicazione:
2021
Abstract:
The HANDS dataset has been created for human-robot interaction research, and it is composed of spatially and temporally aligned RGB and Depth frames. It contains 12 static single-hand gestures performed with both the right-hand and the left-hand, and 3 static two-hands gestures for a total of 29 unique classes. Five actors (two females and three males) have been acquired performing the gestures, each of them adopting a different background and light conditions. For each actor, 150 RGB frames and their corresponding 150 Depth frames per gesture have been collected, for a total of 2400 RGB frames and 2400 Depth frames per actor. Data has been collected using a Kinect v2 camera intrinsically calibrated to spatially align RGB data to Depth data. The temporal alignment has been performed offline using MATLAB, aligning frames with a maximum temporal distance of 66 ms. This dataset has been used in [1] and it is freely available at http://dx.doi.org/10.17632/ndrczc35bt.1.
Tipologia CRIS:
1.1 Articolo in rivista
Keywords:
Classification; Hand-Gesture Recognition; Human-Robot Interaction; Object Detector
Elenco autori:
Nuzzi, C.; Pasinetti, S.; Pagani, R.; Coffetti, G.; Sansoni, G.
Autori di Ateneo:
Nuzzi Cristina
PASINETTI SIMONE
Pagani Roberto
Link alla scheda completa:
https://iris.unibs.it/handle/11379/543676
Link al Full Text:
https://iris.unibs.it/retrieve/handle/11379/543676/141091/2021_HANDS.pdf
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