TY - GEN
T1 - A Decomposition Approach for Complex Gesture Recognition Using DTW and Prefix Tree
AU - Chen, Hui
AU - Ballal, Tarig
AU - Al-Naffouri, Tareq Y.
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2019/8/15
Y1 - 2019/8/15
N2 - Gestures are effective tools for expressing emotions and conveying information to the environment. Sequence matching and machine-learning based algorithm are two main methods to recognize continuous gestures. Machine-learning based recognition systems are not flexible to new gestures because the models have to be trained again. On the other hand, the computational time that matching methods required increases with the complexity and the class of the gestures. In this work, we propose a decomposition approach for complex gesture recognition utilizing DTW and prefix tree. This system can recognize 100 gestures with an accuracy of 97.38%.
AB - Gestures are effective tools for expressing emotions and conveying information to the environment. Sequence matching and machine-learning based algorithm are two main methods to recognize continuous gestures. Machine-learning based recognition systems are not flexible to new gestures because the models have to be trained again. On the other hand, the computational time that matching methods required increases with the complexity and the class of the gestures. In this work, we propose a decomposition approach for complex gesture recognition utilizing DTW and prefix tree. This system can recognize 100 gestures with an accuracy of 97.38%.
UR - http://hdl.handle.net/10754/656575
UR - https://ieeexplore.ieee.org/document/8797868/
UR - http://www.scopus.com/inward/record.url?scp=85071838231&partnerID=8YFLogxK
U2 - 10.1109/vr.2019.8797868
DO - 10.1109/vr.2019.8797868
M3 - Conference contribution
SN - 9781728113777
SP - 876
EP - 877
BT - 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)
PB - IEEE
ER -