@inproceedings{f80fbb5fb2524172bf8b045a42299a58,
title = "Realtime Human-UAV Interaction Using Deep Learning",
abstract = "In this paper, we propose a realtime human gesture identification for controlling a micro UAV in a GPS denied environment. Exploiting the breakthrough of deep convolution network in computer vision, we develop a robust Human-UAV Interaction (HUI) system that can detect and identify a person gesture to control a micro UAV in real time. We also build a new dataset with 23 participants to train or fine-tune the deep neural networks for human gesture detection. Based on the collected dataset, the state-of-art YOLOv2 detection network is tailored to detect the face and two hands locations of a human. Then, an interpreter approach is proposed to infer the gesture from detection results, in which each interpreted gesture is equivalent to a UAV flying command. Real flight experiments performed by non-expert users with the Bebop 2 micro UAV have approved our proposal for HUI. The gesture detection deep model with a demo will be publicly available to aid the research work.",
keywords = "Deep learning, Human gesture, Micro UAV, YOLO",
author = "Ali Maher and Ce Li and Hanwen Hu and Baochang Zhang",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 12th Chinese Conference on Biometric Recognition, CCBR 2017 ; Conference date: 28-10-2017 Through 29-10-2017",
year = "2017",
doi = "10.1007/978-3-319-69923-3\_55",
language = "英语",
isbn = "9783319699226",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "511--519",
editor = "Yunhong Wang and Yu Qiao and Jie Zhou and Jianjiang Feng and Zhenan Sun and Zhenhua Guo and Shiguang Shan and Linlin Shen and Shiqi Yu and Yong Xu",
booktitle = "Biometric Recognition - 12th Chinese Conference, CCBR 2017, Proceedings",
address = "德国",
}