Skip to main content

Advertisement

SpringerLink
Log in

Random bounce algorithm: real-time image processing for the detection of bats and birds

Algorithm description with application examples from a laboratory flight tunnel and a field test at an onshore wind energy plant

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Wind energy plants generate an impact on wildlife with significant fatality rates for various bat and bird species, e.g. due to a collision with the rotor blades. Monitoring approaches, such as vision-based systems, are needed to reduce their mortality by means of an optimized turbine control strategy as soon as flying animals are detected. Since manual analysis of the video data is ineffective, automatic video processing with real-time capabilities is required. In this paper, we propose the random bounce algorithm (RBA) as a novel real-time image processing method for vision-based detection of bats and birds. The RBA is combined with object tracking in order to extract flight trajectories. Its performance is compared with connected components object detection. Results from a laboratory flight tunnel as well as from a field study at a 2 MW wind energy plant in Southern Germany will be presented and discussed. We have successfully detected and tracked objects both in laboratory experiments with many animals and in field experiments with individual animals at a frame rate of 10 fps.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Wang, X.: Intelligent multi-camera video surveillance: a review. Pattern Recognit. Lett. 34(1), 3–19 (2013)

    Article  Google Scholar 

  2. Sivaraman, S., Trivedi, M.M.: A review of recent developments in vision-based vehicle detection. Intelligent Vehicles Symposium (IV), IEEE, (2013), pp. 310–315

  3. Shukla, A.P., Saini, M.: Moving object tracking of vehicle detection: a concise review. Int. J. Signal Process. Image Process. Pattern Recognit. 8(3), 169–176 (2015)

    Article  Google Scholar 

  4. Cetin, A.E., Dimitropoulos, K., Gouverneur, B., Grammalidis, N., Günay, O., Habiboglu, Y.H., Töreyin, B.U., Verstockt, S.: Video fire detection–review. Digit. Signal Process. 23(6), 1827–1843 (2013)

    Article  Google Scholar 

  5. Rydell, J., Engström, H., Hedenström, A., Larsen, J., Pettersson, J., Green, M.: The effect of wind power on birds and bats - A Synthesis, ser. 6511. Swedish Environmental Protection Agency, (2012)

  6. Schuster, E., Bulling, L., Köppel, J.: Consolidating the state of knowledge: a synoptical review of wind energy’s wildlife effects. Environ. Manag. 56(2), 300–331 (2015)

    Article  Google Scholar 

  7. Arnett, E.B., Baerwald, E.F., Mathews, F., Rodrigues, L., Rodríguez-Durán, A., Rydell, J., Villegas-Patraca, R., Voigt, C.C.: Impacts of wind energy development on bats: a global perspective. In: Voigt, C.C., Kingston, T. (eds.) Bats in the Anthropocene: Conservation of Bats in a Changing World, pp. 295–323. Springer International Publishing, Cham (2016)

    Chapter  Google Scholar 

  8. Horn, J.W., Arnett, E.B., Kunz, T.H.: Behavioral responses of bats to operating wind turbines. J. Wildl. Manag. 72(1), 123–132 (2008)

    Article  Google Scholar 

  9. Robinson Willmott, J., Forcey, G.M., Hooton, L.A.: Developing an automated risk management tool to minimize bird and bat mortality at wind facilities. Ambio 44(S4), 557–571 (2015)

    Article  Google Scholar 

  10. Spampinato, C., Farinella, G., Boom, B., Mezaris, V., Betke, M., Fisher, R.B.: Special issue on animal and insect behaviour understanding in image sequences. EURASIP J. Image Video Process. 2015(1), 1 (2015)

    Article  Google Scholar 

  11. Yong, S.-P., Deng, J.D., Purvis, M.K.: Wildlife video key-frame extraction based on novelty detection in semantic context. Multimed. Tools Appl. 62(2), 359–376 (2013)

    Article  Google Scholar 

  12. Calic, J., Campbell, N., Calway, A., Mirmehdi, M., Burghardt, T., Hannuna, S., Kong, C., Porter, S., Canagarajah, N., Bull, D.: Towards intelligent content based retrieval of wildlife videos, in Proceedings of the 6th International Workshop on Image Analysis for Multi-media Interactive Services (WIAMIS’05). Citeseer, (2005)

  13. Christiansen, P., Steen, K., Jørgensen, R., Karstoft, H.: Automated detection and recognition of wildlife using thermal cameras. Sensors 14(8), 13778–13793 (2014)

    Article  Google Scholar 

  14. Burghardt, T., Calic, J.: Analysing animal behaviour in wildlife videos using face detection and tracking. IEEE Proc. Vis. Image Signal Process. 153(3), 305–312 (2006)

    Article  Google Scholar 

  15. Figueroa, K., Camarena-Ibarrola, A., García, J., Villela, H.: Fast automatic detection of wildlife. In: Bayro-Corrochano, E., Hancock, E. (eds.) Images in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, ser. Lecture Notes in Computer Science, pp. 940–947. Springer International Publishing, Cham (2014)

    Google Scholar 

  16. Barnich, O., Van Droogenbroeck, M.: ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans. Image Process. 20(6), 1709–1724 (2011)

    Article  MathSciNet  Google Scholar 

  17. Cullinan, V.I., Matzner, S., Duberstein, C.A.: Classification of birds and bats using flight tracks. Ecol. Inform. 27, 55–63 (2015)

    Article  Google Scholar 

  18. Gómez, A., Salazar, A., Vargas, F.: Towards automatic wild animal monitoring: identification of animal species in camera-trap images using very deep convolutional neural networks, CoRR, abs/1603.06169, 2016

  19. Okuyama, J., Nakajima, K., Matsui, K., Nakamura, Y., Kondo, K., Koizumi, T., Arai, N.: Application of a computer vision technique to animal-borne video data: extraction of head movement to understand sea turtles’ visual assessment of surroundings. Anim. Biotelemetry 3(1), 1–11 (2015)

    Article  Google Scholar 

  20. Gronskyte, R., Clemmensen, L.H., Hviid, M.S., Kulahci, M.: Pig herd monitoring and undesirable tripping and stepping prevention. Comput. Electron. Agric. 119, 51–60 (2015)

    Article  Google Scholar 

  21. Nasirahmadi, A., Richter, U., Hensel, O., Edwards, S., Sturm, B.: Using machine vision for investigation of changes in pig group lying patterns. Comput. Electron. Agric. 119, 184–190 (2015)

    Article  Google Scholar 

  22. Gronskyte, R., Clemmensen, L.H., Hviid, M.S., Kulahci, M.: Monitoring pig movement at the slaughterhouse using optical flow and modified angular histograms. Biosyst. Eng. 141, 19–30 (2016)

    Article  Google Scholar 

  23. van Gemert, J.C., Verschoor, C.R., Mettes, P., Epema, K., Koh, L.P., Wich, S.: Nature conservation drones for automatic localization and counting of animals. in Computer Vision-ECCV, : Workshops. Springer, 255–270 (2014)

  24. Gonzalez, L., Montes, G., Puig, E., Johnson, S., Mengersen, K., Gaston, K.: Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation. Sensors 16(1), 97 (2016)

    Article  Google Scholar 

  25. Ebner, B., Starrs, D., Morgan, D., Fulton, C., Donaldson, J., Doody, J., Cousins, S., Kennard, M., Butler, G., Tonkin, Z., Beatty, S., Broadhurst, B., Clear, R., Lintermans, M., Fletcher, C.: Emergence of field-based underwater video for understanding the ecology of freshwater fishes and crustaceans in Australia. J. R. Soc. West. Aust. 97, 287–296 (2014)

    Google Scholar 

  26. Kashiha, M.A., Green, A.R., Sales, T.G., Bahr, C., Berckmans, D., Gates, R.S.: Performance of an image analysis processing system for hen tracking in an environmental preference chamber. Poult. Sci. 93(10), 2439–2448 (2014)

    Article  Google Scholar 

  27. Rowcliffe, J.M., Jansen, P.A., Kays, R., Kranstauber, B., Carbone, C.: Wildlife speed cameras: measuring animal travel speed and day range using camera traps. Remote Sens. Ecol. Conserv. (2016)

  28. Moeslund, T.B., Granum, E.: A survey of computer vision-based human motion capture. Comput. Vis. Image Underst. 81(3), 231–268 (2001)

    Article  MATH  Google Scholar 

  29. Hedenström, A., Johansson, L.C., Spedding, G.R.: Bird or bat: comparing airframe design and flight performance. Bioinspir. Biomim. 4(1), 015001 (2009)

    Article  Google Scholar 

  30. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409–1422 (2012)

    Article  Google Scholar 

  31. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Upper Saddle River (2002)

    Google Scholar 

  32. He, Lifeng, Yuyan, Chao, Suzuki, K.: A run-based two-scan labeling algorithm. IEEE Trans. Image Process. 17(5), 749–756 (2008)

    Article  MathSciNet  Google Scholar 

  33. Stewart, P.D., Ellwood, S.A., Macdonald, D.W.: Remote video-surveillance of wildlife -an introduction from experience with the European badger meles meles. Mamm. Rev. 27(4), 185–204 (1997)

    Article  Google Scholar 

  34. Brown, J., Gehrt, S.D.: The Basics of Using Remote Cameras to Monitor Wildlife, Ohio State University Extension Agriculture and Natural Resources Fact Sheet W-21-09 Ohio Sate University. OH, Columbus (2009)

    Google Scholar 

  35. Kays, R., Tilak, S., Kranstauber, B., Jansen, P.A., Carbone, C., Rowcliffe, M.J., Fountain, T., Eggert, J., He, Z.: Monitoring wild animal communities with arrays of motion sensitive camera traps. arXiv:1009.5718 (2010)

  36. Foster, R.J., Harmsen, B.J.: A critique of density estimation from camera-trap data. J. Wildl. Manag. 76(2), 224–236 (2012)

    Article  Google Scholar 

  37. Moll, J., Mälzer, M., Scholz, B., Krozer, V., Pozdniakov, D., Salman, R., Zimmermann, R., Hechavarria, J., Beetz, J., Kössl, M.: Radar-based detection of bats: experiments in a laboratory flight tunnel. In: 10th European Conference on Antennas and Propagation Davos, Switzerland. doi:10.1109/EuCAP.2016.7481643 (2016)

  38. Moll, J., Mälzer, M., Scholz, N., Krozer, V., Dürr, M., Pozdniakov, D., Salman, R., Zimmermann, R., Scholz, M.: Radar-based detection of birds near wind energy plants: first experiences from a field study. In: 10th German Microwave Conference, pp. 239–242. doi:10.1109/GEMIC.2016.7461600 (2016)

Download references

Acknowledgments

This work is part of the B\(^2\)-Monitor project “Millimeter-Waves for Monitoring Bats and Blades” and is financially supported by the Federal Ministry for Economic Affairs and Energy (grant number: FKZ 0325791A). More information can be found at http://www.b2monitor.de. The authors are grateful to Mr. Dürr (Volta Windkraft GmbH, Ochsenfurt, Germany) for the installation of the camera system at the wind energy plant. Moreover, the authors would like to thank Prof. Kössl (Goethe University of Frankfurt, Institute for Cell Biology and Neuroscience) for the bat experiments in the laboratory flight tunnel.

Author information

Authors and Affiliations

  1. Goethe University of Frankfurt am Main, Frankfurt, Germany

    Nikolas Scholz, Jochen Moll, Moritz Mälzer, Konstantin Nagovitsyn & Viktor Krozer

Authors
  1. Nikolas Scholz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jochen Moll
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Moritz Mälzer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Konstantin Nagovitsyn
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Viktor Krozer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jochen Moll.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Scholz, N., Moll, J., Mälzer, M. et al. Random bounce algorithm: real-time image processing for the detection of bats and birds. SIViP 10, 1449–1456 (2016). https://doi.org/10.1007/s11760-016-0951-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-016-0951-0

Keywords

Search

Navigation