Fast Aerial UAV Detection Based on Image Segmentation and HOG-FLD Feature Fusion

DOI

10.2991/icsnce-18.2018.33How to use a DOI?

Keywords

Image Segmentation; Graph Theory; HOG; FLD; SVM

Abstract

In order to detect non-cooperative target UAV quickly and accurately, a novel method of UAV detection method based on graph theory and HOG-FLD feature fusion is presented in this paper. In order to avoid the time-consuming full search, the candidate areas of the UAV are obtained through the selective search of the image segmentation and the similarity, and the features are extracted through the method of gradient orientation histogram fusion FLD linear to train the SVM classifier with generalization ability to identify the UAV. The method can detect the UAV quickly and accurately under complicated background and circumstances of various position and angle. Compared with the sliding window method based on image segmentation and HOG+SVM, the experimental results show that the speed of this method has been obviously improved with the same recognition accuracy.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Li Xiaoping
AU  - Lei Songze
AU  - Wang Yanhong
AU  - Xiao Feng
AU  - Tian Penghui
PY  - 2018/04
DA  - 2018/04
TI  - Fast Aerial UAV Detection Based on Image Segmentation and HOG-FLD Feature Fusion
BT  - Proceedings of the 2018 Second International Conference of Sensor Network and Computer Engineering (ICSNCE 2018)
PB  - Atlantis Press
SP  - 162
EP  - 170
SN  - 2352-538X
UR  - https://doi.org/10.2991/icsnce-18.2018.33
DO  - 10.2991/icsnce-18.2018.33
ID  - Xiaoping2018/04
ER  -