Skip to main content

A Computer Vision and Control Algorithm to Follow a Human Target in a Generic Environment Using a Drone

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9773))

Abstract

This work proposes an innovative technique to solve the problem of tracking and following a generic human target by a drone in a natural, possibly dark scene. The algorithm does not rely on color information but mainly on shape information, using the HOG classifier, and on local brightness information, using the optical flow algorithm. We tried to keep the algorithm as light as possible, envisioning its future application on embedded or mobile devices. After several tests, performed modeling the system as a set of SISO feedback-controlled systems and calculating the Integral Squared Error as quality indicator, we noticed that the final performance, overall satisfactory, degrades as the background complexity and the presence of disturbance sources, such as sharp edges and moving objects that cross the target, increase .

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Higuchi, K., Shimada, T., Rekimoto, J.: Flying sports assistant: external visual imagery. In: AH, 12–14 March 2011

    Google Scholar 

  2. Nagi, J., Giusti, A., Di Caro, G.A., Gambardella, L.M.: Human control of UAVs using face pose estimates and hand gestures. In: HRI, 03–06 March 2014

    Google Scholar 

  3. Kos’myna, N., Tarpin-Bernard, F., Rivet, B.: Bidirectional feedback in motor imagery BCIs: learn to control a drone within 5 minutes. In: CHI, 26 April–1 May 2014

    Google Scholar 

  4. Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600 (1994)

    Google Scholar 

  5. Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, pp. 147–151 (2001)

    Google Scholar 

  6. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, vol. 1, pp. 886–893, 25 June 2005

    Google Scholar 

  7. Miyoshi, K., Konomura, R., Hori, K.: Above Your Hand: direct and natural interaction with aerial robot. In: ACM, 10–14 August 2014

    Google Scholar 

  8. Hansen, J.P., Alapetite, A., Scott MacKenzie, I., Møllenbach, E.: The use of gaze to control drones. In: ETRA, 26–28 March 2014

    Google Scholar 

  9. Pittman, C., LaViola Jr., J.J.: Exploring head tracked head mounted displays for first person robot teleoperation. In: IUI, 24–27 February 2014

    Google Scholar 

  10. Pfeil, K.P., Koh, S.L., LaViola Jr., J.J.: Exploring 3D gesture metaphors for interaction with unmanned aerial vehicles. In: IUI, 19–22 March 2013

    Google Scholar 

  11. Mueller, F., Muirhead, M.: Understanding the design of a flying jogging companion. In: UIST, 05–08 October 2014

    Google Scholar 

  12. Mueller, F., Muirhead, M.: Jogging with a Quadcopter. In: CHI 2015 Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 2023–2032, 18 August 2015

    Google Scholar 

  13. Sefidgari, B.L.: Feed-back method based on image processing for detecting human body via flying robot. Int. J. Artif. Intell. Appl. (IJAIA) 4(6), 35–44 (2013)

    Google Scholar 

  14. Munoz, C.A., Sobh, T.M.: Object tracking using autonomous Quad Copter. Robotics, Intelligent Sensing & Control (RISC) Lab., University of Bridgeport, 28 March 2014

    Google Scholar 

  15. Liang, N.S., Wan Yusoff, W.A., Dhinesh, R., Sak, J.S.: Low cost night vision system for intruder detection. In: IOP Conference Series: Materials Science and Engineering, vol. 114(1) (2016)

    Google Scholar 

  16. Jeong, M.R., Kwak, J.Y., Son, J.E., Ko, B., Nam, J.Y.: Fast pedestrian detection using a night vision system for safety driving. In: 2014 11th International Conference on (IEEE) Computer Graphics, Imaging and Visualization, CGIV, pp. 69–72 (2014)

    Google Scholar 

  17. Kimura, M., Shibasaki, R., Shao, X., Nagai, M.: Automatic extraction of moving objects from uav-borne monocular images using multi-view geometric constraints. In: IMAV 2014: International Micro Air Vehicle Conference and Competition 2014, Delft, The Netherlands, 12–15 August 2014

    Google Scholar 

  18. Chan, W.S.: Autonomous Quadcopter Flight System with Object Tracking. Department of Electronic Engineering, Undergraduate Final Year Projects, City University of Hong Kong (2015)

    Google Scholar 

  19. Mercado, D.A., Castillo, P., Lozano, R.: Quadrotor’s trajectory tracking control using monocular vision navigation. In: 2015 International Conference on Unmanned Aircraft Systems (ICUAS), 9–12 June 2015

    Google Scholar 

  20. Boudjit, K., Larbes, C.: Detection and implementation autonomous target tracking with a Quadrotor AR.Drone. In: 2015 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO), vol. 02, 21–23 July 2015

    Google Scholar 

  21. Harik, E.H.C., Guérin, F., Guinand, F., Brethé, J.F., Pelvillain, H., Zentout, A.: Vision based target tracking using an unmanned aerial vehicle. In: 2015 IEEE International Workshop on Advanced Robotics and its Social Impacts, ARSO 2015, Lyon, France, July 2015

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vitoantonio Bevilacqua .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Bevilacqua, V., Di Maio, A. (2016). A Computer Vision and Control Algorithm to Follow a Human Target in a Generic Environment Using a Drone. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42297-8_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42296-1

  • Online ISBN: 978-3-319-42297-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics