TY - GEN
T1 - A survey on dynamic hand gesture recognition using kinect device
AU - Ikram, Aamrah
AU - Liu, Yue
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd., 2018.
PY - 2018
Y1 - 2018
N2 - In Human Computer Interface (HCI) technology, Hand Gestures Recognition (HGR) is a diverse field. In Dynamic Hand gesture recognition (DHGR), an unprecedented work has been done over few decades and it is still growing day by day. HGR has been extensively used in other scopes like biomedical, gaming and entertainment, research and monitoring etc. Because of its versatile utility, HGR is getting popular among the people, as it is making HCI more efficient, natural and user friendly. For the purpose of accurate segmentation and tracking a controller free and fascinating device, Kinect was introduced. In this paper Kinect based algorithms are discussed and addressed. Algorithms for DHGR are compared and particularly focused on Hidden Markov Model (HMM) and Support Vector Machine (SVM). At the end, it is observed that recognition accuracy improved significantly with Kinect device due to its good interactive features, efficiency and accuracy.
AB - In Human Computer Interface (HCI) technology, Hand Gestures Recognition (HGR) is a diverse field. In Dynamic Hand gesture recognition (DHGR), an unprecedented work has been done over few decades and it is still growing day by day. HGR has been extensively used in other scopes like biomedical, gaming and entertainment, research and monitoring etc. Because of its versatile utility, HGR is getting popular among the people, as it is making HCI more efficient, natural and user friendly. For the purpose of accurate segmentation and tracking a controller free and fascinating device, Kinect was introduced. In this paper Kinect based algorithms are discussed and addressed. Algorithms for DHGR are compared and particularly focused on Hidden Markov Model (HMM) and Support Vector Machine (SVM). At the end, it is observed that recognition accuracy improved significantly with Kinect device due to its good interactive features, efficiency and accuracy.
KW - Gesture recognition
KW - Hidden Markov model
KW - Human computer interaction
KW - Kinect device
KW - Support Vector Machine
UR - http://www.scopus.com/inward/record.url?scp=85052223986&partnerID=8YFLogxK
U2 - 10.1007/978-981-13-1702-6_63
DO - 10.1007/978-981-13-1702-6_63
M3 - Conference contribution
AN - SCOPUS:85052223986
SN - 9789811317019
T3 - Communications in Computer and Information Science
SP - 635
EP - 646
BT - Image and Graphics Technologies and Applications - 13th Conference on Image and Graphics Technologies and Applications, IGTA 2018, Revised Selected Papers
A2 - Wang, Yongtian
A2 - Peng, Yuxin
A2 - Jiang, Zhiguo
PB - Springer Verlag
T2 - 13th Conference on Image and Graphics Technologies and Applications, IGTA 2018
Y2 - 8 April 2018 through 10 April 2018
ER -