@inproceedings{1956b1a5a2c04d8f89c276a02cc4a822,
title = "Deep-learning-based moving target detection for unmanned air vehicles",
abstract = "In this paper, a deep learning network is investigated to detect moving targets for a UAV equipped with monocular camera. An algorithm based on fully convolutional network is proposed to obtain the position and moving direction of targets. A Kalman filter is incorporated into the proposed algorithm to increase the accuracy of target position information acquisition. The experimental results show the effectiveness of the proposed algorithm with a relatively low hardware resource consumption.",
keywords = "Kalman filter, deep learning, fully convolutional network, unmanned air vehicle",
author = "Haodi Yao and Qingtao Yu and Xiaowei Xing and Fenghua He and Jie Ma",
note = "Publisher Copyright: {\textcopyright} 2017 Technical Committee on Control Theory, CAA.; 36th Chinese Control Conference, CCC 2017 ; Conference date: 26-07-2017 Through 28-07-2017",
year = "2017",
month = sep,
day = "7",
doi = "10.23919/ChiCC.2017.8029186",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "11459--11463",
editor = "Tao Liu and Qianchuan Zhao",
booktitle = "Proceedings of the 36th Chinese Control Conference, CCC 2017",
address = "美国",
}