Skip to main content

Automated Decision Making in Road Traffic Monitoring by On-board Unmanned Aerial Vehicle System

  • Chapter
  • First Online:
Computer Vision in Control Systems-3

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 135))

Abstract

The study is dedicated to solving the target issues of the ground traffic monitoring aided by the Unmanned Aerial Vehicles (UAV) based on applying the on-board computer vision systems. The classification of the road situations using images obtained after Traffic Accident (TA) is based on the feature set, facts, and attributes specified directly and/or indirectly on a possible situation class. The hierarchical structure of description of a road situation observable after the TA event is developed. For decision making, the production model of knowledge representation and corresponding Knowledge Base (KB) is offered to use. The issues related to decision making for recognition of the occurring traffic situations have been considered. The analysis of the strategies have been carried out based on the principles of minimizing the overall losses, limiting the admissible UAV flight altitude, and ensuring the required class recognition reliability. The models describing the functional criteria of the losses, flight safety of the UAV, and reliability of class recognition have been proposed. It has been shown that applying the minimum loss criterion ensures considerable savings of resources under different ratio of the loss quotients. The example for classification of a road incident using the real images is given.

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

Access this chapter

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
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

References

  1. Türmer, S., Leitloff, J., Reinartz, P., Stilla, U.: Evaluation of selected features for car detection in aerial images. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVIII-4/W19, pp. 341–346 (2011)

    Google Scholar 

  2. Qadir, A., Semke, W., Neubert, J.: Implementation of an onboard visual tracking system with small unmanned aerial vehicle (UAV). Int. J. Innov. Technol. Creat. Eng. 1(10), 1–9 (2011)

    Google Scholar 

  3. Kim, N., Chervonenkis, M.: Situational control unmanned aerial vehicles for traffic monitoring. Mod. Appl. Sci. 9(5), 1913–1852 (2015)

    Google Scholar 

  4. Rainer, L., Maydt, J.: An extended set of Haar-like features for rapid object detection. In: International Conference on Image Processing (ICIP’2002), vol. 1, pp. 900–903 (2002)

    Google Scholar 

  5. Zhang, J., Liu, L., Wang, B., Chen, X., Wang, Q., Zheng, T.: High speed automatic power line detection and tracking for a UAV-based inspection. In: International Conference on Industrial Control and Electronics Engineering (ICICEE’2012), pp. 266–269 (2012)

    Google Scholar 

  6. Forssyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. Prentice Hall, Ptr. Copyright by Pearson Education, Inc. (2003)

    Google Scholar 

  7. Bernd, J.: Digital Image Processing. Springer, Berlin, Heidelberg (2005)

    Google Scholar 

  8. Kim, N., Bodunkov, N.: Adaptive surveillance algorithms based on the situation analysis. In: Favorskaya, M., Jain, L.C. (eds.) Computer Vision in Control Systems-2, ISRL, vol. 75, pp. 169–200 (2015)

    Google Scholar 

  9. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 13.1–13.45 (2006)

    Google Scholar 

  10. Lin, F., Lum, K.Y., Chen, B.M., Lee, T.H.: Development of a vision-based ground target detection and tracking system for a small unmanned helicopter. Sci. China Ser. F: Inf. Sci. 52, 2201–2215 (2009)

    Article  MATH  Google Scholar 

  11. Lin, Y., Yu, Q., Medioni, G.: Efficient detection and tracking of moving objects in geo-coordinates. Mach. Vis. Appl. 22(3), 505–520 (2011)

    Google Scholar 

  12. Kim, N.: Automated decision making in road traffic monitoring by on-board unmanned aerial vehicle system. Ind. J. Sci. Technol. 8(S10), 1–6 (2015)

    Google Scholar 

  13. Gorelik, A.L.: Recognition Methods. Vishaya shkola, Moscow (in Russian) (2004)

    Google Scholar 

  14. Pospelov, D.A.: Situational Control: Theory and Practice. Nauka, Moscow (in Russian) (1986)

    Google Scholar 

  15. Li, L., Jiang, S., Huang, Q.: Learning hierarchical semantic description via mixed-norm regularization for image understanding. IEEE Trans. Multimedia 14(5), 1401–1413 (2012)

    Article  Google Scholar 

  16. Guarino, N., Oberle, D., Staab, S.: What is an ontology? In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. International Handbooks on Information Systems, pp. 1–17 (2009)

    Google Scholar 

  17. Mizoguchi, R.: Tutorial on ontological engineering: Part 3: Advanced course of ontological engineering. New Gener. Comput. 22(2), 193–220 (2004)

    Article  MATH  Google Scholar 

  18. Yu, T.H., Moon, Y.S.: Unsupervised abnormal behavior detection for real-time surveillance using observed history. Adv. Biomet. 1019–1029 (2009)

    Google Scholar 

  19. Zhu, Y.Y., Zhu, Y.Y., Zhen-Kun, W., Chen, W.S., Huang, Q.: Detection and recognition of abnormal running behavior in surveillance video. Math. Probl. Eng. 296407, 1–14 (2012)

    MATH  Google Scholar 

  20. Yang, K., Cai, Z., Zhao, L.: Algorithm research on moving object detection of surveillance video sequence. Sci. Res. 3(28), 308–312 (2013)

    Google Scholar 

  21. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)

    Article  Google Scholar 

  22. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)

    Article  Google Scholar 

  23. Implementation of the SLIC superpixel algorithm to work with OpenCV. http://docs.opencv.org/trunk/df/d6c/group__ximgproc__superpixel.html. Accessed 12 June 2017

  24. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2004)

    Article  Google Scholar 

  25. Zhong, S., Ghosh, J.: A unified framework for model-based clustering. J. Mach. Learn. Res. 4, 1001–1037 (2003)

    MATH  MathSciNet  Google Scholar 

  26. Meuel, H., Reso, M., Jachalsky, J., Ostermann, J.: Superpixel-based segmentation of moving objects for low bitrate ROI coding systems. In: 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS’2013), pp. 27–30 (2013)

    Google Scholar 

  27. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR’2005), vol. 1, pp. 886–893 (2005)

    Google Scholar 

  28. Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21–45 (2006)

    Article  Google Scholar 

  29. Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends Signal Process. 7(3–4), 197–387 (2014)

    Article  MATH  MathSciNet  Google Scholar 

  30. Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Inc., New York, NY, USA (1995)

    MATH  Google Scholar 

  31. Farnebäck, G.: Very high accuracy velocity estimation using orientation tensors, parametric motion, and simultaneous segmentation of the motion field. In: 8th IEEE International Conference on Computer Vision (ICCV‘2001), vol. 1, pp. 171–177 (2001)

    Google Scholar 

  32. Welch, G., Bishop, G.: An Introduction to the Kalman Filter. Technical report, University of North Carolina at Chapel Hill Chapel Hill, NC, USA (1995)

    Google Scholar 

  33. Kleeman, L.: Understanding and applying Kalman filtering. In: 2nd Workshop on Perceptive Systems, pp. 1–4 (1996)

    Google Scholar 

  34. Kelly, A.: A 3D State Space Formulation of a Navigation Kalman Filter for Autonomous Vehicles. CMU-RI-TR-94-19-REV 2.0, Carnegie Mellon University (1994)

    Google Scholar 

  35. Hazewinkel, M. (ed.): Encyclopedia of Mathematics (set). Kluwer Academic Publishers, Dordrechr, Holland (1988)

    Google Scholar 

  36. Neyman, J., Pearson, E.S.: On the problem of the most efficient tests of statistical hypotheses. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 231, 289–337 (1933)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nikolay Kim .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Kim, N., Bodunkov, N. (2018). Automated Decision Making in Road Traffic Monitoring by On-board Unmanned Aerial Vehicle System. In: Favorskaya, M., Jain, L. (eds) Computer Vision in Control Systems-3. Intelligent Systems Reference Library, vol 135. Springer, Cham. https://doi.org/10.1007/978-3-319-67516-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67516-9_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67515-2

  • Online ISBN: 978-3-319-67516-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics