DEEP LEARNING TECHNIQUE - BASED DRONE DETECTION AND TRACKING
301 viewsAbstract
The usage of small drones/UAVs is becoming increasingly important in recent years. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. This paper resolves the problem of detecting small drones in surveillance videos using deep learning algorithms. Single Shot Detector (SSD) object detection algorithm and MobileNet-v2 architecture as the backbone were used for our experiments. The pre-trained model was re-trained on custom drone synthetic dataset by using transfer learning’s fine-tune technique. The results of detecting drone in our experiments were around 90.8%. The combination of drone detection, Dlib correlation tracking algorithm and centroid tracking algorithm effectively detects and tracks the small drone in various complex environments as well as is able to handle multiple target appearances.
References
[1]. Ulzhalgas Seidaliyeva, Daryn Akhmetov, Lyazzat Ilipbayeva, Eric T. Matson “Real-Time and Accurate Drone Detection in a Video with a Static Background”, Sensors 2020, 20, 3856; doi:10.3390/s20143856.
[2]. Michael Jian, Zhenzhong Lu and Victor C. Chen, “Drone Detection and Tracking Based on Phase-Interferometric Doppler Radar”, 2018 IEEE Radar Conference.
[3]. Dongkyu ’Roy’ Lee, Woong Gyu La, and Hwangnam Kim, “Drone Detection and Identification System using Artificial Intelligence”, 2018 International Conference on Information and Communication Technology Convergence (ICTC).
[4]. J. Janousek, P. Marcon, J. Pokorny, and J. Mikulka, “Detection and Tracking of Moving UAVs”, 2019 Photonics Electromagnetics Research Symposium.
[5]. A. Howard et al., "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications", Computing Research Repositor, arXiv:1704.04861, 2017.
[6]. Dlib C++ Library, (2018) "Correlation Tracker," .[Online]. Available:
http://dlib.net/imaging.html#correlationtracker.
[7]. Adrian Rosebrock, Simple object tracking with OpenCV, Available at:https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with opencv.
[8]. Yujie Du, Mingyu Gao, Yuxiang Yang, Jing Zhang2, Zhongfei Yu, “A Target Detection System for Mobile Robot Based On Single Shot Multibox Detector Neural Network”, 2018 IEEE 4th International Conference on Control Science and Systems Engineering.
[9]. Hashir Ali, Mahrukh Khursheed, Syeda Kulsoom Fatima, “Object Recognition for Dental Instruments Using SSD-MobileNet”, 2019 International Conference on Information Science and Communication Technology (ICISCT).
[10. Brad Dwyer, "How to Create a Synthetic Dataset for Computer Vision", https://blog.roboflow.com.
[11]. Priya Dwivedi (2017). “Is Google Tensorflow Object Detection API the easiest way to implement image recognition?”. Available at: https://towardsdatascience.com/is-google-tensorflow-object-detection-api-the-easiest-way-to-implementimage-recognition-a8bd1f500ea0.
[12]. G. Gamage, I. Sudasingha, I. Perera, D. Meedeniya, “Reinstating Dlib Correlation Human Trackers Under Occlusions in Human Detection based Tracking”, 2018 International Conference on Advances in ICT for Emerging Regions (ICTer) : 092 – 098.
[13. Lasitha Mekkayil, Hariharan Ramasangu, “Object Tracking with Correlation Filters using Selective Single Background”, arXiv:1805.03453v1 [cs.CV] 9 May 2018.
[14]. Adrian Rosebrock, OpenCV People Counter Available at : https://www.pyimagesearch.com/https://www.pyimagesearch.com/2018/08/13/opencv-people-counter.
[15]. B. Keni and S. Rainer, “Evaluating multiple object tracking performance: the clear mot metrics”, EURASIP J. Image Video Process, Dec. 2008.
