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Cascade Improved Visual Detection and Distance Estimation in Formation of Micro Unmanned Aerial Vehicles

  • Jiankun Sun
  • Yanxuan WuEmail author
  • Xutan Lu
  • Yunduo Feng
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 582)

Abstract

According to the localization of UAVs in formation, this paper has proposed a visual based method using cascade classifiers of LBP and Haar-like features to realize UAVs’ detection and distance estimation. Since the phenomenon of overlapping in formation has an obvious influence on the performance of detection, this paper adopts the method of integrating two classifiers with different features (LBP and Haar) to improve the performance of detector. For the purpose of enhancing the precision of distance estimation, an optimized method is proposed based on the prediction of UAV’s attitude. The description of coordination is deducted to obtain accurately relative position from visual information. The results of test videos verify the performance of proposed method on the application of UAVs formation.

Keywords

Localization UAVs formation Cascade 

Notes

Acknowledgments

Foundation program: supported by Key Lab Fund of Shanxi province (XJZZ201704) and National Natural Fund (41419060201).

References

  1. 1.
    Ham, Y., Han, K.K., Lin, J.J., et al.: Visual monitoring of civil infrastructure systems via camera-equipped unmanned aerial vehicles (UAVs): a review of related works [J]. Vis. Eng. 4(1), 1 (2016)Google Scholar
  2. 2.
    Ian, L.T., Mitchell, D.H., Christopher, D.D.: UAVs for coastal surveying. Coast. Eng. 114, 19–24 (2016)CrossRefGoogle Scholar
  3. 3.
    Nieuwenhuisen, M., Droeschel, D., Beul, M., et al.: Autonomous navigation for micro aerial vehicles in complex GNSS-denied environments. J. Intell. Rob. Syst. 84(1–4), 199–216 (2016)CrossRefGoogle Scholar
  4. 4.
    Saska, M., Baca, T., Thomas, J., et al.: System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization. Auton. Robots 41(4), 919–944 (2017)CrossRefGoogle Scholar
  5. 5.
    Walter, V., Saska, M., Franchi, A.: Fast mutual relative localization of uavs using ultraviolet led markers. In: 2018 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1217–1226. IEEE (2018)Google Scholar
  6. 6.
    Faigl, J, Krajník, T, Chudoba, J., et al.: Low-cost embedded system for relative localization in robotic swarms. In: 2013 IEEE International Conference on Robotics and Automation, pp. 993–998. IEEE (2013)Google Scholar
  7. 7.
    Aker, C., Kalkan, S.: Using deep networks for drone detection. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6. IEEE (2017)Google Scholar
  8. 8.
    Lin, F., Peng, K., Dong, X., et al.: Vision-based formation for UAVs. In: 11th IEEE International Conference on Control & Automation (ICCA), pp. 1375–1380. IEEE (2014)Google Scholar
  9. 9.
    Rozantsev, A., Lepetit, V., Fua, P.: Detecting flying objects using a single moving camera. IEEE Trans. Pattern Anal. Mach. Intell. 39(5), 879–892 (2017)CrossRefGoogle Scholar
  10. 10.
    Li, J., Ye, D.H., Chung, T., et al.: Multi-target detection and tracking from a single camera in unmanned aerial vehicles (UAVs). In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4992–4997. IEEE (2016)Google Scholar
  11. 11.
    Opromolla, R., Fasano, G., Accardo, D.: A vision-based approach to UAV detection and tracking in cooperative applications. Sensors 18(10), 3391 (2018)CrossRefGoogle Scholar
  12. 12.
    Gökçe, F., Üçoluk, G., Şahin, E., et al.: Vision-based detection and distance estimation of micro unmanned aerial vehicles. Sensors 15(9), 23805–23846 (2015)CrossRefGoogle Scholar
  13. 13.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  14. 14.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  15. 15.
    Liao, S., Zhu, X., Lei, Z., et al.: Learning multi-scale block local binary patterns for face recognition. In: International Conference on Biometrics, pp. 828–837. Springer, Berlin, Heidelberg (2007)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Jiankun Sun
    • 1
  • Yanxuan Wu
    • 1
    Email author
  • Xutan Lu
    • 1
  • Yunduo Feng
    • 1
  1. 1.Beijing Institute of TechnologyBeijingChina

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