TY - GEN
T1 - MaHG-RGBD
T2 - 2019 IEEE International Conference on Consumer Electronics, ICCE 2019
AU - Liu, Jia Qing
AU - Furusawa, Kotaro
AU - Tsujinaga, Seiju
AU - Tateyama, Tomoko
AU - Iwamoto, Yutaro
AU - Chen, Yen Wei
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/3/6
Y1 - 2019/3/6
N2 - Here, we present a new dataset, named MaHG-RGBD, including 25 hand gestures performed by 15 participants as viewed from multiple angles. This dataset is intended to train models for deep-learning-based hand-gesture recognition. Unlike existing datasets, MaHG-RGBD includes not only front views (tilt angle = 0) but also the titled views (tilt angle = 45 degrees), which are often needed when there are space constraints. In addition, the dataset includes pairs of synchronized color and depth images of the hand region that are well segmented. Users can utilize just one of the image modalities or both depending on the application. This dataset includes a wide variety of different gestures classes: a total of 25 hand gestures. We evaluate the recognition accuracy of 25 different hand gestures using deep learning as a benchmark with this dataset. The MaHG-RGBD dataset is available at http://www.iipl.is.ritsumei.ac.jp/MaHG-RGBD.
AB - Here, we present a new dataset, named MaHG-RGBD, including 25 hand gestures performed by 15 participants as viewed from multiple angles. This dataset is intended to train models for deep-learning-based hand-gesture recognition. Unlike existing datasets, MaHG-RGBD includes not only front views (tilt angle = 0) but also the titled views (tilt angle = 45 degrees), which are often needed when there are space constraints. In addition, the dataset includes pairs of synchronized color and depth images of the hand region that are well segmented. Users can utilize just one of the image modalities or both depending on the application. This dataset includes a wide variety of different gestures classes: a total of 25 hand gestures. We evaluate the recognition accuracy of 25 different hand gestures using deep learning as a benchmark with this dataset. The MaHG-RGBD dataset is available at http://www.iipl.is.ritsumei.ac.jp/MaHG-RGBD.
UR - https://www.scopus.com/pages/publications/85063767979
UR - https://www.scopus.com/pages/publications/85063767979#tab=citedBy
U2 - 10.1109/ICCE.2019.8661941
DO - 10.1109/ICCE.2019.8661941
M3 - Conference contribution
AN - SCOPUS:85063767979
T3 - 2019 IEEE International Conference on Consumer Electronics, ICCE 2019
BT - 2019 IEEE International Conference on Consumer Electronics, ICCE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 January 2019 through 13 January 2019
ER -