Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications
Sonain Jamil,
Fawad,
MuhibUr Rahman,
Amin Ullah,
Salman Badnava,
Masoud Forsat,
Seyed Sajad Mirjavadi
Affiliations
Sonain Jamil
ACTSENA Research Group, Telecommunication Engineering Department, University of Engineering and Technology, Taxila, Punjab 47050, Pakistan
Fawad
ACTSENA Research Group, Telecommunication Engineering Department, University of Engineering and Technology, Taxila, Punjab 47050, Pakistan
MuhibUr Rahman
Department of Electrical Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada
Amin Ullah
College of Engineering & Computer Science (CECS), Center for Research in Computer Vision Lab (CRCV Lab), University of Central Florida (UCF), Orlando, FL 32816, USA
Salman Badnava
Department of Computer Science and Engineering, College of Engineering, Qatar University, P.O. Box Doha 2713, Qatar
Masoud Forsat
Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, P.O. Box Doha 2713, Qatar
Seyed Sajad Mirjavadi
Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, P.O. Box Doha 2713, Qatar
Unmanned aerial vehicles (UAVs) have become popular in surveillance, security, and remote monitoring. However, they also pose serious security threats to public privacy. The timely detection of a malicious drone is currently an open research issue for security provisioning companies. Recently, the problem has been addressed by a plethora of schemes. However, each plan has a limitation, such as extreme weather conditions and huge dataset requirements. In this paper, we propose a novel framework consisting of the hybrid handcrafted and deep feature to detect and localize malicious drones from their sound and image information. The respective datasets include sounds and occluded images of birds, airplanes, and thunderstorms, with variations in resolution and illumination. Various kernels of the support vector machine (SVM) are applied to classify the features. Experimental results validate the improved performance of the proposed scheme compared to other related methods.
