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Implementation of Autonomous Unmanned Aerial Vehicle with Moving-Object Detection and Face Recognition

  • Sanjaa Bold
  • Batchimeg Sosorbaram
  • Seong Ro Lee
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 376)

Abstract

Various personal robots have developed for performing useful tasks autonomously without the human interaction. The development of human navigation and tracking in the real time environment will lead to the implementation of more advanced tasks that can performed by the autonomous robots. This project proposes a system that can track objects using Speeded Up Robust Feature (SURF) detection and human face recognition using Eigenface recognizer. In this paper, combinations of algorithms such as modified Eigenface, Haar-Cascade classifier and SURF resulted in a more robust system for object detection and face recognition. The proposed system was implemented on AR drone 2.0 using the Microsoft Visual Studio 2010 platform together with OpenCV. The testing of the proposed system carried out in an indoor environment in order to evaluate its performance in terms of detection distance, angle of detection, and accuracy of detection. 132 images of different people were used for face recognition at four different detection distances. The best average result of 92.22% was obtained at a detection distance of 50cm to 75cm. It noted that the detection accuracy decreases as the detection distance is increased. Furthermore, improve various limitations, strengths of the AR drone and the proposed algorithm.

Keywords

Eigenface recognizer PCA Haar-Cascade AR drone 2.0 

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References

  1. 1.
    Bold, S., Lee, S.R.: FPGA based real time embedded color tracking mobile robot. In: ICITCS 2013. IEEE Society (2013)Google Scholar
  2. 2.
    Achtelik, M., Zhang, T.: Visual tracking and control of a quadcopter using a stereo camera system and inertial sensor. In: IEEE Mechatronics and Automation, International Conference (ICMA 2009), pp 2863–2869, August 2009Google Scholar
  3. 3.
    Using a Stereo Camera System and Inertial Sensors (ISARC 2009), pp. 252–258 (2009)Google Scholar
  4. 4.
    Pounds, P., Mahony, R., Gresham, J., Corke, P., Roberts, J.: Towards dynamically-favourable quad- rotor aerial robots. In: Australian Conference on Robotics and Automation (2004)Google Scholar
  5. 5.
    Hoffmann, G., Huang, H., Waslander, S.L., Tomlin, C.J.: Quadrotor helicopter flight dynamics and control: theory and experiment. In: Proceedings of the AIAA Guidance, Navigation, and Control Conference (2007)Google Scholar
  6. 6.
    Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: IEEE Computer Vision and Pattern Recognition, pp. 1297–1304, June 2011Google Scholar
  7. 7.
    Liu, S., Wang, Y., Yuan, L., Bu, J., Tan, P., Sun, J.: Video stabilization with a depth camera. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 89–95, June 2012Google Scholar
  8. 8.
    Wenzel, K.E., Masselli, A., Zell, A.: Automatic take off, tracking and landing of a miniature uav on a moving carrier vehicle. Journal of Intelligent & Robotic Systems 61, 221–238 (2011)CrossRefGoogle Scholar
  9. 9.
    Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Pattern Analysis and Machine Intelligence 19, 711–720 (1997)CrossRefGoogle Scholar
  10. 10.
    Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Pattern Analysis and Machine Intelligence 23, 228–233 (2001)CrossRefGoogle Scholar
  11. 11.
    Yuen, P.C., Lai, J.H.: Face representation using independent component analysis. Pattern Recognition 35, 1247–1257 (2002)CrossRefzbMATHGoogle Scholar
  12. 12.
    Perlibakas, V.: Distance Measures for PCA-based Face Recognition. Pattern Recognition Letters 25(6), 711–724 (2004)CrossRefGoogle Scholar
  13. 13.
    Bold, S.: Autonomous Vision-Based Face Recognition System for Unmanned Aerial Vehicles. Scopus Journal, Mongolia, Chinggis Khaan Hotel (2014)Google Scholar
  14. 14.
    Zhang, S.: Object tracking in Unmanned Aerial Vehicle (UAV) videos using a combined approach. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 681–684 (2005)Google Scholar
  15. 15.
    Theodorakopoulos, P., Lacroix, S.: Uav target tracking using an adversarial iterative prediction. In: IEEE International Conference on Robotics and Automation, Kobe, pp. 2866–2871, May 2009Google Scholar
  16. 16.
    Bold, S.: Autonomous Vision-Based Moving Object Detection for Unmanned Aerial Vehicle. Scopus Journal. Korea (2014)Google Scholar
  17. 17.
    Altug, E., Ostrowski, J.P., Mahony, R.: Control of a quadrotor helicopter using visual feedback. In: IEEE International Conference on Robotics and Automation, vol. 1, pp. 72–77 (2002)Google Scholar
  18. 18.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: European Conference on Computer Vision, ECCV, Austria, vol. 3951, pp. 404–417 (2006)Google Scholar
  19. 19.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding 110, 346–359 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Sanjaa Bold
    • 1
    • 2
  • Batchimeg Sosorbaram
    • 1
    • 2
  • Seong Ro Lee
    • 1
    • 2
  1. 1.Department of Computer Hardware and NetworkUniversity of the HumanitiesUlaanbaatarMongolia
  2. 2.Department of Electronic EngineeringMokpo National UniversityMokpoSouth Korea

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