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.
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References
Wang, X.: Intelligent multi-camera video surveillance: a review. Pattern Recognit. Lett. 34(1), 3–19 (2013)
Sivaraman, S., Trivedi, M.M.: A review of recent developments in vision-based vehicle detection. Intelligent Vehicles Symposium (IV), IEEE, (2013), pp. 310–315
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Christiansen, P., Steen, K., Jørgensen, R., Karstoft, H.: Automated detection and recognition of wildlife using thermal cameras. Sensors 14(8), 13778–13793 (2014)
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)
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)
Barnich, O., Van Droogenbroeck, M.: ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans. Image Process. 20(6), 1709–1724 (2011)
Cullinan, V.I., Matzner, S., Duberstein, C.A.: Classification of birds and bats using flight tracks. Ecol. Inform. 27, 55–63 (2015)
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
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)
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)
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)
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)
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)
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)
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)
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)
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)
Moeslund, T.B., Granum, E.: A survey of computer vision-based human motion capture. Comput. Vis. Image Underst. 81(3), 231–268 (2001)
Hedenström, A., Johansson, L.C., Spedding, G.R.: Bird or bat: comparing airframe design and flight performance. Bioinspir. Biomim. 4(1), 015001 (2009)
Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409–1422 (2012)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Upper Saddle River (2002)
He, Lifeng, Yuyan, Chao, Suzuki, K.: A run-based two-scan labeling algorithm. IEEE Trans. Image Process. 17(5), 749–756 (2008)
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)
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)
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)
Foster, R.J., Harmsen, B.J.: A critique of density estimation from camera-trap data. J. Wildl. Manag. 76(2), 224–236 (2012)
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)
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)
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.
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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
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DOI: https://doi.org/10.1007/s11760-016-0951-0