TY - GEN
T1 - Deep learning based hand gesture recognition and UAV flight controls
AU - Hu, Bin
AU - Wang, Jiacun
N1 - Publisher Copyright:
© 2018 Chinese Automation and Computing Society in the UK - CACSUK.
PY - 2018/9
Y1 - 2018/9
N2 - Dynamic hand gesture recognition is desired as an alternative means for human-computer interactions. This paper presents a hand gesture recognition system that is designed for the control of flights of unmanned aerial vehicles (UAV). To train the system to recognize designed gestures, skeleton data collected from a Leap Motion Controller are converted to two different data models. As many as 9124 samples of training dataset, 1938 samples of testing dataset are created to train and test the proposed three deep learning neural networks, which are a 2-layer fully connected neural network, a 5-layer fully connected neural network and an 8-layer convolutional neural network. The static testing results show that the 2-layer fully connected neural network achieves an average accuracy of 98.2% on normalized datasets and 11% on raw datasets. The 5-layer fully connected neural network achieves an average accuracy of 95.2% on normalized datasets and 45% on raw datasets. The 8-layers convolutional neural network achieves an average accuracy of 96.2% on normalized datasets and raw datasets. Testing on a drone-kit simulator and a real drone shows that this system is feasible for drone flight controls.
AB - Dynamic hand gesture recognition is desired as an alternative means for human-computer interactions. This paper presents a hand gesture recognition system that is designed for the control of flights of unmanned aerial vehicles (UAV). To train the system to recognize designed gestures, skeleton data collected from a Leap Motion Controller are converted to two different data models. As many as 9124 samples of training dataset, 1938 samples of testing dataset are created to train and test the proposed three deep learning neural networks, which are a 2-layer fully connected neural network, a 5-layer fully connected neural network and an 8-layer convolutional neural network. The static testing results show that the 2-layer fully connected neural network achieves an average accuracy of 98.2% on normalized datasets and 11% on raw datasets. The 5-layer fully connected neural network achieves an average accuracy of 95.2% on normalized datasets and 45% on raw datasets. The 8-layers convolutional neural network achieves an average accuracy of 96.2% on normalized datasets and raw datasets. Testing on a drone-kit simulator and a real drone shows that this system is feasible for drone flight controls.
KW - Deep learning
KW - Drones
KW - Hand gesture recognition
KW - Leap motion controllers
KW - Neural networks
UR - http://www.scopus.com/inward/record.url?scp=85069199938&partnerID=8YFLogxK
U2 - 10.23919/IConAC.2018.8748953
DO - 10.23919/IConAC.2018.8748953
M3 - Conference contribution
AN - SCOPUS:85069199938
T3 - ICAC 2018 - 2018 24th IEEE International Conference on Automation and Computing: Improving Productivity through Automation and Computing
BT - ICAC 2018 - 2018 24th IEEE International Conference on Automation and Computing
A2 - Ma, Xiandong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th IEEE International Conference on Automation and Computing, ICAC 2018
Y2 - 6 September 2018 through 7 September 2018
ER -