Vision-Based Target Following

Part of the Advances in Industrial Control book series (AIC)


Finally, in Chap. 11, we document the design and implementation of a comprehensive vision system for an unmanned rotorcraft to realize missions such as ground target detection and following. To realize the autonomous ground target seeking and following, a sophisticated vision algorithm is proposed to detect the target and estimate relative distance to the target using an onboard color camera together with necessary navigation sensors. The vision feedback is then integrated with the automatic flight control system to guide the unmanned helicopter to follow the ground target inflight. The overall vision system is tested in actual flight missions, and the results obtained show that it is robust and efficient.


Discriminant Function Intrinsic Parameter Search Window Camera Frame Image Tracking 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 4.
    Amidi O, Kanade T, Miller R. Vision-based autonomous helicopter research at Carnegie Mellon Robotics Institute 1991–1997. In: Proc American helicopter society int conf, Gifu, Japan; 1998. p. 1–12. Google Scholar
  2. 8.
    Bradski GR, Clara S. Computer vision face tracking for use in a perceptual user interface. Intel Technol J. 1998;Q2:1–15. Google Scholar
  3. 10.
    Boykov Y, Huttenlocher DP. Adaptive Bayesian recognition in tracking rigid objects. In: Proc IEEE conf computer vision pattern recognition, Hilton Head, SC; 2000. p. 697–704. Google Scholar
  4. 20.
    Cai G, Lin F, Chen BM, Lee TH. Systematic design methodology and construction of UAV helicopters. Mechatronics. 2008;18:545–58. CrossRefGoogle Scholar
  5. 23.
    Camera calibration toolbox for MATLAB. Cited Aug 2010.
  6. 25.
    Chaumette F, Hutchinson S. Visual servo control part I: basic approaches. IEEE Robot Autom Mag. 2006;13:82–90. CrossRefGoogle Scholar
  7. 26.
    Chaumette F, Hutchinson S. Visual servo control part II: advanced approaches. IEEE Robot Autom Mag. 2007;14:109–18. CrossRefGoogle Scholar
  8. 31.
    Cheng Y. Mean shift, mode seeking, and clustering. IEEE Trans Pattern Anal Mach Intell. 1995;17:790–9. CrossRefGoogle Scholar
  9. 34.
    Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell. 2003;25:564–77. CrossRefGoogle Scholar
  10. 59.
    Foley JD, Vandam A, Feiner SK, Hughes JF. Fundamentals of interactive computer graphics. Reading: Addison Wesley; 1990. Google Scholar
  11. 60.
    Fowers SG, Lee DJ, Tippetts BJ, Lillywhite KD, Dennis AW, Archibald JK. Vision aided stabilization and the development of a quad-rotor micro UAV. In: Proc 2007 IEEE int symposium computational intell robot automat, Jacksonville, FL; 2007. p. 143–8. CrossRefGoogle Scholar
  12. 62.
    Franklin G, Powell FD, Naeini AE. Feedback control of dynamic systems. 4th ed. Upper Saddle River: Prentice Hall; 2002. Google Scholar
  13. 70.
    Gonzalez G, Woods RE. Digital image processing. Reading: Addison Wesley; 1992. Google Scholar
  14. 71.
    Guenard N, Hamel T, Mahony R. A practical visual servo control for an unmanned aerial vehicle. IEEE Trans Robot. 2008;24:331–40. CrossRefGoogle Scholar
  15. 77.
    He R, Bachrach A, Achtelik M, et al. On the design and use of a micro air vehicle to track and avoid adversaries. Int J Robot Res. 2010;29:529–46. CrossRefGoogle Scholar
  16. 80.
    Heikkila J, Silven O. A four-step camera calibration procedure with implicit image correction. In: Proc 1997 conf computer vision pattern recognition. Puerto Rico: San Juan; 1997. p. 1106–12. CrossRefGoogle Scholar
  17. 82.
    Hrabar S, Sukhatme GS, Corke P, Usher K, Roberts J. Combined optic-flow and stereo-based navigation of urban canyons for a UAV. In: Proc IEEE/RSJ int conf intell robot syst, Edmonton, Canada; 2005. p. 3309–16. CrossRefGoogle Scholar
  18. 84.
    Isard M, Blake A. Condensation conditional density propagation for visual tracking. Int J Comput Vis. 1998;29:5–28. CrossRefGoogle Scholar
  19. 90.
    Johnson EN, Calise AJ, Watanabe Y, Ha J, Neidhoefer JC. Real-time vision-based relative aircraft navigation. J Aerosp Comput Inf Commun. 2007;4:707–38. CrossRefGoogle Scholar
  20. 97.
    Kim J, Sukkarieh S. SLAM aided GPS/INS navigation in GPS denied and unknown environments. In: Proc int symposium GNSS/GPS, Sydney, Australia; 2004. Google Scholar
  21. 107.
    Li XR, Jilkov VP. Survey of maneuvering target tracking, part I: dynamic models. IEEE Trans Aerosp Electron Syst. 2003;39:1333–64. CrossRefGoogle Scholar
  22. 108.
    Lin F. Development and applications of a vision-based unmanned helicopter [PhD dissertation]. Dept of Electrical and Computer Engineering, National University of Singapore; 2011. Google Scholar
  23. 109.
    Lin F, Chen BM, Lum KY, Lee TH. A robust vision system on an unmanned helicopter for ground target seeking and following. In: Proc 8th world congress intell contr automat, Jinan, China; 2010. p. 276–81. CrossRefGoogle Scholar
  24. 110.
    Lin F, Lum KY, Chen BM, Lee TH. Development of a vision-based ground target detection and tracking system for a small unmanned helicopter. Sci China Ser F: Inf Sci. 2009;52:2201–15. CrossRefMATHGoogle Scholar
  25. 113.
    Ma Y, Soatto S, Kosecka J, Sastry SS. An invitation to 3-D vision: from images to geometric models. New York: Springer; 2004. MATHGoogle Scholar
  26. 119.
    Meingast M, Geyer C, Sastry S. Vision based terrain recovery for landing unmanned aerial vehicles. In: Proc IEEE conf dec contr, Atlantis, Bahamas; 2004. p. 1670–5. Google Scholar
  27. 120.
    Mejias LO, Saripalli S, Cervera P, Sukhatme GS. Visual servoing of an autonomous helicopter in urban areas using feature tracking. J Field Robot. 2006;23:185–99. CrossRefGoogle Scholar
  28. 135.
    NUS UAV research. Cited Aug 2010.
  29. 145.
    Plataniotis KN, Venetsanopoulos AN. Color image processing and applications. New York: Springer; 2000. Google Scholar
  30. 146.
    Porikli F. Achieving real-time object detection and tracking under extreme conditions. J Real-Time Image Process. 2006;1:33–40. CrossRefGoogle Scholar
  31. 151.
    Rodriguez PA, Geckle WJ, Barton JD, Samsundar J, Gao T, Brown MZ, Martin SR. An emergency response UAV surveillance system. In: Proc AMIA annual symposium, Washington, DC; 2006. p. 1078. Google Scholar
  32. 155.
    Schell FR, Dickmanns ED. Autonomous landing of airplanes by dynamic machine vision. Mach Vis Appl. 1994;7:127–34. CrossRefGoogle Scholar
  33. 159.
    Shakernia O, Ma Y, Koo TJ, Sastry S. Landing an unmanned air vehicle: vision based motion estimation and nonlinear control. Asian J Control. 1999;1:128–45. CrossRefGoogle Scholar
  34. 160.
    Shakernia O, Vidal R, Sharp CS, Ma Y, Sastry S. Multiple view motion estimation and control for landing an unmanned aerial vehicle. In: Proc IEEE int conf robot automat, Washington, DC; 2002. p. 2793–8. Google Scholar
  35. 161.
    Sharp CS, Shakernia O, Sastry S. A vision system for landing an unmanned aerial vehicle. In: Proc IEEE int conf robot automat, Seoul, Korea; 2001. p. 1720–7. Google Scholar
  36. 165.
    Shih YT. The reversibility of six geometric color spaces. Photogramm Eng Remote Sens. 1995;61:1223–32. Google Scholar
  37. 169.
    Smith AR. Color gamut transform pairs. In: Proc 5th annual conf computer graphics interactive techniques, New York; 1978. p. 12–9. Google Scholar
  38. 189.
    Veeraraghavan H, Schrater P, Papanikolopoulos N. Robust target detection and tracking through integration of motion, color and geometry. Comput Vis Image Underst. 2006;103:121–38. CrossRefGoogle Scholar
  39. 201.
    Wikipedia. Color space. Cited Aug 2010.
  40. 216.
    Wren CR, Azarbayejani A, Darrell T, Pentland AP. Pfinder: real-time tracking of the human body. IEEE Trans Pattern Anal Mach Intell. 1997;19:780–5. CrossRefGoogle Scholar
  41. 224.
    Zhu Q, Avidan S, Cheng KT. Flexible camera calibration by viewing a plane from unknown orientations. In: Proc 7th IEEE int conf computer vision, Kerkyra, Greece; 1999. p. 666–73. Google Scholar
  42. 225.
    Zhou QM, Aggarwalb JK. Object tracking in an outdoor environment using fusion of features and cameras. Image Vis Comput. 2006;24:1244–55. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Temasek LaboratoriesNational University of SingaporeSingaporeSingapore
  2. 2.Dept. Electrical & Computer EngineeringNational University of SingaporeSingaporeSingapore

Personalised recommendations