Small UAV Detection in Videos from a Single Moving Camera

The rapid application of Unmamned Aerial Vehicles (UAV) has triggered serious threats to public security, individual privacy, military security, etc. Thus, discovering unknown UAVs fast and reliably becomes more and more important. Among UAV detection techniques, the vision-based method is almost the lowest cost and the most easily-configured one. In this paper, we propose a UAV detection method based on a single moving camera to handle the problem for UAVs with fast moving speed. Firstly, we employ a motion estimation method to stabilize videos. Then, a low-rank based model is adopted to obtain the object proposals and finally, a CNN-SVM approach is used to further confirm real UAV objects. Two real UAV datasets are used to evaluate the proposed method and experimental results show that our method outperforms the baseline methods.

[1]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[3]  Xiaowei Zhou,et al.  Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Peter Wellig,et al.  Detection and tracking of drones using advanced acoustic cameras , 2015, SPIE Security + Defence.

[5]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Junghyun Park,et al.  Real-time UAV sound detection and analysis system , 2017, 2017 IEEE Sensors Applications Symposium (SAS).

[7]  Pascal Fua,et al.  Flight Dynamics-Based Recovery of a UAV Trajectory Using Ground Cameras , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[9]  Luc Van Gool,et al.  Efficient Non-Maximum Suppression , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[10]  Pei Li,et al.  People counting based on head detection combining Adaboost and CNN in crowded surveillance environment , 2016, Neurocomputing.

[11]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Yi Yang,et al.  Infrared Patch-Image Model for Small Target Detection in a Single Image , 2013, IEEE Transactions on Image Processing.

[13]  Richard Han,et al.  Investigating Cost-effective RF-based Detection of Drones , 2016, DroNet@MobiSys.

[14]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[15]  Richard Han,et al.  Matthan: Drone Presence Detection by Identifying Physical Signatures in the Drone's RF Communication , 2017, MobiSys.

[16]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Rahul Sukthankar,et al.  Fast and accurate global motion compensation , 2011, Pattern Recognit..

[18]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[19]  Xiaokang Yang,et al.  HEASK: Robust homography estimation based on appearance similarity and keypoint correspondences , 2014, Pattern Recognit..

[20]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[21]  Andrew Zisserman,et al.  MLESAC: A New Robust Estimator with Application to Estimating Image Geometry , 2000, Comput. Vis. Image Underst..

[22]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[23]  Yi Ma,et al.  Robust principal component analysis? , 2009, JACM.

[24]  Miguel A. Olivares-Méndez,et al.  On-board and Ground Visual Pose Estimation Techniques for UAV Control , 2011, J. Intell. Robotic Syst..

[25]  Vincent Lepetit,et al.  Flying objects detection from a single moving camera , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Xudong Jiang,et al.  Regularized 2-D complex-log spectral analysis and subspace reliability analysis of micro-Doppler signature for UAV detection , 2017, Pattern Recognit..