(41-4) 12 * << * >> * Русский * English * Содержание * Все выпуски

Aerial vehicles detection and recognition for UAV vision system
Muraviev V.S., Smirnov S.A., Strotov V.V.

Ryazan State Radio Engineering University, Ryazan, Russia

 PDF 656 kB

DOI: 10.18287/2412-6179-2017-41-4-545-551

Страницы: 545-551.

Abstract:
This article focuses on aerial vehicle detection and recognition by a wide field of view monocular vision system that can be installed on UAVs (unmanned aerial vehicles). The objects are mostly observed on the background of clouds under regular daylight conditions. The main idea is to create a multi-step approach based on a preliminary detection, regions of interest (ROI) selection, contour segmentation, object matching and localization. The described algorithm is able to detect small targets, but unlike many other approaches is designed to work with large-scale objects as well. The suggested algorithm is also intended to recognize and track the aerial vehicles of specific kind using a set of reference objects defined by their 3D models. For that purpose a computationally efficient contour descriptor for the models and the test objects is calculated. An experimental research on real video sequences is performed. The video database contains different types of aerial vehicles: airplanes, helicopters, and UAVs. The proposed approach shows good accuracy in all case studies and can be implemented in onboard vision systems.

Keywords:
aerial vehicles, object detection, contour descriptor, recognition.

Citation:
Muraviev VS, Smirnov SA, Strotov VV. Aerial vehicles detection and recognition for UAV vision systems. Computer Optics 2017; 41(4): 545-551. DOI: 10.18287/2412-6179-2017-41-4-
545-551.

References:

  1. Tirri AE, Fasano G, Accardo D, Moccia A, De Leis E. Advanced Sensing Issues for UAS Collision Avoidance // Proceedings of the 2nd International Conference on Application and Theory of Automation in Command and Control Systems 2012; 12-19.
  2. Lai JS, Mejias L, Ford JJ. Airborne vision?based collision?detection system. Journal of Field Robotics 2010; 28(2): 137-157. DOI: 10.1002/rob.20359.
  3. Nussberger A, Grabner H, Gool LV. Aerial object tracking from an airborne platform. International Conference on Unmanned Aircraft Systems (ICUAS) 2014; 1284-1293.
  4. Kovács L, Benedek C. Visual real-time detection, recognition and tracking of ground and airborne targets. Proc SPIE 2011: 7873: 787311. DOI: 10.1117/12.872314.
  5. Kovács L, Kovács A, Utasi Á, Szirányi T. Flying target detection and recognition by feature fusion. Optical Engineering 2012; 51(11): 117002. DOI: 10.1117/1.OE.51.11.117002.
  6. Alpatov B, Korepanov S, Strotov V. A composite algorithm for variable size object tracking for high performance FPGA-based on-board vision systems. Proc SPIE 2014; 9247: 92470A. DOI: 10.1117/12.2064058.
  7. Shotton J, Blake A, Cipolla R. Multi-scale categorical object recognition using contour fragments. IEEE Transactions on Pattern Analysis and Machine Intelligence 2008; 30(7): 1270-1281. DOI: 10.1109/TPAMI.2007.70772.
  8. Shen W, et al. Deepcontour: A deep convolutional feature learned by positive-sharing loss for contour detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015; 3982-3991. DOI: 10.1109/CVPR.2015.7299024.
  9. Chan TF, Vese LA. Active contours without edges. IEEE Transactions on Image Processing 2001; 10(2): 266-277. DOI: 10.1109/83.902291.
  10. Wang XF, Huang DS, Xu H. An efficient local ChanVese model for image segmentation. Pattern Recognition 2010; 43(3): 603-618. DOI: 10.1016/j.patcog.2009.08.002.
  11. Arkin EM, Chew LP, Huttenlocher DP, Kedem K, Mitchell JS. An efficiently computable metric for comparing polygonal shapes IEEE Transactions on Pattern Analysis and Machine Intelligence 1991; 13(3): 209-216. DOI: 10.1109/34.75509.
  12. Alpatov BA, Babayan PV, Ershov MD, Strotov VV. The implementation of contour-based object orientation estimation algorithm in FPGA-based on-board vision system. Proc SPIE 2016; 10007: 100070A. DOI: 10.1117/12.2241091.
  13. Petridis S, Geyer C, Singh S. Learning to detect aircraft at low resolutions. Proceedings of the 6th International Conference Computer Vision Systems (ICVS 2008); 474-483.
  14. Dey D, Geyer CM, Singh S, Digioia M. A cascaded method to detect aircraft in video imagery. The International Journal of Robotics Research 2011; 30(12): 1527-1540.
  15. Kovacs L, Benedek C. Visual real-time detection, recognition and tracking of ground and airborne targets. Proc SPIE 2011; 7873: 787311. DOI: 10.1117/12.872314.
  16. Rong HJ, Jia YX, Zhao GS. Aircraft recognition using modular extreme learning machine. Neurocomputing 2014; 128: 166-174. DOI: 10.1016/j.neucom.2012.12.064.
  17. Li X-D, Pan J-D, Dezert J. Automatic aircraft recognition using DSmT and HMM. 17th International Conference on Information Fusion 2014.
  18. Karine A, Toumi A, Khenchaf A, El Hassouni M. Aircraft recognition using a statistical model and sparse representation //Proceedings of the International Conference on Big Data and Advanced Wireless Technologies 2016; 49. DOI: 10.1145/3010089.3010134.

© 2009, IPSI RAS
Россия, 443001, Самара, ул. Молодогвардейская, 151; электронная почта: journal@computeroptics.ru ; тел: +7 (846) 242-41-24 (ответственный секретарь), +7 (846) 332-56-22 (технический редактор), факс: +7 (846) 332-56-20
Institution of Russian Academy of Sciences, Image Processing Systems Institute of RAS, Russia, 443001, Samara, Molodogvardeyskaya Street 151; E-mail: journal@computeroptics.ru; Phones: +7 (846) 332-56-22, Fax: +7 (846) 332-56-20