Communication publiée dans un ouvrage (Colloques, congrès, conférences scientifiques et actes)
A Preliminary Study on the Automatic Visual based Identification of UAV Pilots from Counter UAVs
Cazzato, Dario; CIMARELLI, Claudio; VOOS, Holger
2020 • In 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, Valletta 27-29 February 2020
Computer Vision; Unmanned Aerial Vehicles; Pilot indentification
Résumé :
[en] Two typical Unmanned Aerial Vehicles (UAV) countermeasures involve the detection and tracking of the UAV position, as well as of the human pilot; they are of critical importance before taking any countermeasure, and they already obtained strong attention from national security agencies in different countries. Recent advances in computer vision and artificial intelligence are already proposing many visual detection systems from an operating UAV, but they do not focus on the problem of the detection of the pilot of another approaching unauthorized UAV. In this work, a first attempt of proposing a full autonomous pipeline to process images from a flying UAV to detect the pilot of an unauthorized UAV entering a no-fly zone is introduced. A challenging video sequence has been created flying with a UAV in an urban scenario and it has been used for this preliminary evaluation. Experiments show very encouraging results in terms of recognition, and a complete dataset to evaluate artificial intelligence-based solution will be prepared.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
Cazzato, Dario
CIMARELLI, Claudio ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation
VOOS, Holger ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
A Preliminary Study on the Automatic Visual based Identification of UAV Pilots from Counter UAVs
Date de publication/diffusion :
27 février 2020
Nom de la manifestation :
15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP
Organisateur de la manifestation :
INSTICC
Lieu de la manifestation :
Valletta, Malte
Date de la manifestation :
27-02-2020 to 29-02-2020
Manifestation à portée :
International
Titre de l'ouvrage principal :
15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, Valletta 27-29 February 2020
Abdulla, W. (2017). Mask r-cnn for object detection and instance segmentation on keras and tensorflow. https://github.com/matterport/Mask RCNN.
Bergstra, J. and Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(Feb):281–305.
Biallawons, O., Klare, J., and Fuhrmann, L. (2018). Improved uav detection with the mimo radar mira-cle ka using range-velocity processing and tdma correction algorithms. In 2018 19th International Radar Symposium (IRS), pages 1–10. IEEE.
Bisio, I., Garibotto, C., Lavagetto, F., Sciarrone, A., and Zappatore, S. (2018). Unauthorized amateur uav detection based on wifi statistical fingerprint analysis. IEEE Communications Magazine, 56(4):106–111.
Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., and Sheikh, Y. (2018). OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields. In arXiv preprint arXiv:1812.08008.
Carnie, R., Walker, R., and Corke, P. (2006). Image processing algorithms for uav” sense and avoid”. In Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., pages 2848–2853. IEEE.
Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3):273–297.
Deloitte (2018). Unmanned aircraft systems (uas) risk management: Thriving amid emerging threats and opportunities.
Ezuma, M., Erden, F., Anjinappa, C. K., Ozdemir, O., and Guvenc, I. (2019). Micro-uav detection and classification from rf fingerprints using machine learning techniques. In 2019 IEEE Aerospace Conference, pages 1–13. IEEE.
Gaspar, J., Ferreira, R., Sebastião, P., and Souto, N. (2018). Capture of uavs through gps spoofing. In 2018 Global Wireless Summit (GWS), pages 21–26. IEEE.
Guvenc, I., Koohifar, F., Singh, S., Sichitiu, M. L., and Matolak, D. (2018). Detection, tracking, and interdiction for amateur drones. IEEE Communications Magazine, 56(4):75–81.
Han, J., Pei, J., and Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
Hartmann, K. and Steup, C. (2013). The vulnerability of uavs to cyber attacks-an approach to the risk assessment. In 2013 5th international conference on cyber conflict (CYCON 2013), pages 1–23. IEEE.
He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961–2969.
Jovanoska, S., Brötje, M., and Koch, W. (2018). Multisensor data fusion for uav detection and tracking. In 2018 19th International Radar Symposium (IRS), pages 1–10. IEEE.
Kerns, A. J., Shepard, D. P., Bhatti, J. A., and Humphreys, T. E. (2014). Unmanned aircraft capture and control via gps spoofing. Journal of Field Robotics, 31(4):617–636.
Kim, I. S., Choi, H. S., Yi, K. M., Choi, J. Y., and Kong, S. G. (2010). Intelligent visual surveillancea survey. International Journal of Control, Automation and Systems, 8(5):926–939.
Korobiichuk, I., Danik, Y., Samchyshyn, O., Dupelich, S., and Kachniarz, M. (2019). The estimation algorithm of operative capabilities of complex countermeasures to resist uavs. Simulation, 95(6):569–573.
Kyrkou, C., Plastiras, G., Theocharides, T., Venieris, S. I., and Bouganis, C.-S. (2018). Dronet: Efficient convolutional neural network detector for real-time uav applications. In 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), pages 967–972. IEEE.
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. (2014). Microsoft coco: Common objects in context. In European conference on computer vision, pages 740–755. Springer.
Liu, J., Shahroudy, A., Perez, M. L., Wang, G., Duan, L.-Y., and Chichung, A. K. (2019). Ntu rgb+ d 120: A large-scale benchmark for 3d human activity understanding. IEEE transactions on pattern analysis and machine intelligence.
May, R., Steinheim, Y., Kvaløy, P., Vang, R., and Hanssen, F. (2017). Performance test and verification of an off-the-shelf automated avian radar tracking system. Ecology and evolution, 7(15):5930–5938.
Mazur, M., Wisniewski, A., and McMillan, J. (2016). Clarity from above: Pwc global report on the commercial applications of drone technology. Warsaw: Drone Powered Solutions, PriceWater house Coopers.
Morris, B. T. and Trivedi, M. M. (2008). A survey of vision-based trajectory learning and analysis for surveillance. IEEE transactions on circuits and systems for video technology, 18(8):1114–1127.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Platt, J. et al. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers, 10(3):61–74.
Poppe, R. (2010). A survey on vision-based human action recognition. Image and vision computing, 28(6):976–990.
Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems, pages 91–99.
Shakhatreh, H., Sawalmeh, A. H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., Othman, N. S., Khreishah, A., and Guizani, M. (2019). Unmanned aerial vehicles (uavs): A survey on civil applications and key research challenges. IEEE Access, 7:48572–48634.
Shoufan, A., Al-Angari, H. M., Sheikh, M. F. A., and Damiani, E. (2018). Drone pilot identification by classifying radio-control signals. IEEE Transactions on Information Forensics and Security, 13(10):2439–2447.
Soomro, K., Zamir, A. R., and Shah, M. (2012). Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402.
Su, J., He, J., Cheng, P., and Chen, J. (2016). A stealthy gps spoofing strategy for manipulating the trajectory of an unmanned aerial vehicle. IFAC-PapersOnLine, 49(22):291–296.
Unlu, E., Zenou, E., and Riviere, N. (2018). Using shape descriptors for uav detection. Electronic Imaging, 2018(9):1–5.
Unlu, E., Zenou, E., Riviere, N., and Dupouy, P.-E. (2019). Deep learning-based strategies for the detection and tracking of drones using several cameras. IPSJ Transactions on Computer Vision and Applications, 11(1):7.
Wagoner, A. R., Schrader, D. K., and Matson, E. T. (2017). Survey on detection and tracking of uavs using computer vision. In 2017 First IEEE International Conference on Robotic Computing (IRC), pages 320–325. IEEE.
Zhang, H., Cao, C., Xu, L., and Gulliver, T. A. (2018). A uav detection algorithm based on an artificial neural network. IEEE Access, 6:24720–24728.
Zhang, T. and Zhu, Q. (2017). Strategic defense against deceptive civilian gps spoofing of unmanned aerial vehicles. In International Conference on Decision and Game Theory for Security, pages 213–233. Springer.
