Fast Aerial UAV Detection Based on Image Segmentation and HOG-FLD Feature Fusion
- 10.2991/icsnce-18.2018.33How to use a DOI?
- Image Segmentation; Graph Theory; HOG; FLD; SVM
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.
- © 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/).
Cite this article
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 -