Skip to main content

UAVs Applied to the Counting and Monitoring of Animals

  • Conference paper

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 291)

Abstract

The advantages of intelligent approaches such as the conjunction of artificial vision and the use of Unmanned Aerial Vehicles (UAVs) have been recently emerging. This paper presents a focused on obtaining scans of large areas of livestock system. Counting and monitoring of animal species can be performed with video recordings taken from UAVs. Moreover the system keeps track of the number of animals detected by analyzing the images taken with the UAVs cameras. Several tests have been performed to evaluate this system and preliminary results and the conclusions are presented in this paper.

Keywords

  • Unmanned Aerial Vehicle
  • Convolutional Neural Networks
  • livestock detection

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-07596-9_8
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-07596-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   219.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. San Miguel, A., Ochoay, J., Pérez Carral, C.: Wildlife Management in Mediterranean Forest Ecosystems. Problems and current situation of Shrublands. Montes (35), 33–36 (1994)

    Google Scholar 

  2. B. T. a. C. J.: Real-time Face Detection and Tracking of Animals. In: de 8th Seminar of Neural Network Applications in Electrical Engineering, Belgrade (2006)

    Google Scholar 

  3. Buenestado, B.V.: Aprovechamiento y gestión de la caza en España. Una reflexión a propósito de los cercados cinegéticos. Actas del VI Coloquio de Geografía Rural, 257–272 (1991)

    Google Scholar 

  4. Bodin, W.K., Redman, J.J., Thorson, D.C.: U.S. Patent No. 7,286,913. U.S. Patent and Trademark Office, Washington, DC (2007)

    Google Scholar 

  5. Bottou, L.: Stochastic learning. In: Bousquet, O., von Luxburg, U., Rätsch, G. (eds.) Machine Learning 2003. LNCS (LNAI), vol. 3176, pp. 146–168. Springer, Heidelberg (2004)

    CrossRef  Google Scholar 

  6. Buss, H., Busker, I.: Mikrokopter (2008)

    Google Scholar 

  7. C. S. a. R. T. J. McKinlay: Integrating Count Effort by Seasonally Correcting Animal Population. CCAMLR Science 17, 213–227 (2010)

    Google Scholar 

  8. Pinzón, C.I., Bajo, J., De Paz, J.F., Corchado, J.M.: S-MAS: An adaptive hierarchical distributed multi-agent architecture for blocking malicious SOAP messages within Web Services environments. Expert Systems with Applications 38(5), 5486–5499

    Google Scholar 

  9. Tapia, D.I., Abraham, A., Corchado, J.M., Alonso, R.S.: Agents and ambient intelligence: case studies. Journal of Ambient Intelligence and Humanized Computing 1(2), 85–93 (2010)

    CrossRef  Google Scholar 

  10. Tapia, D.I., De Paz, J.F., Rodríguez, S., Bajo, J., Corchado, J.M.: Multi-agent system for security control on industrial environments. International Transactions on System Science and Applications Journal 4(3), 222–226 (2008)

    Google Scholar 

  11. Tapia, D.I., Alonso, R.S., De Paz, J.F., Corchado, J.M.: Introducing a distributed architecture for heterogeneous wireless sensor networks. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009, Part II. LNCS, vol. 5518, pp. 116–123. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  12. Tapia, D.I., Rodríguez, S., Bajo, J., Corchado, J.M.: FUSION@, a SOA-based multi-agent architecture. In: International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008), pp. 99–107 (2008)

    Google Scholar 

  13. Fdez-Riverola, F., Corchado, J.M.: CBR based system for forecasting red tides. Knowledge-Based Systems 16(5), 321–328 (2003)

    CrossRef  MathSciNet  Google Scholar 

  14. Fukushima, K.: Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36(4), 193–202 (1980), doi:10.1007/BF00344251

    CrossRef  MATH  Google Scholar 

  15. Sileshi, G.: The excess-zero problem in soil animal count data and choice of appropriate models for statistical inference. Pedobiologia 52(1), 1–17 (2008)

    CrossRef  Google Scholar 

  16. Garijo, F., Gómes-Sanz, J.J., Pavón, J., Massonet, P.: Multi-agent system organization: An engineering perspective. In: Pre-Proceeding of the 10th European Workshop on Modeling Autonomous Agents in a Multi-Agent World (MAAMAW 2001) (2001)

    Google Scholar 

  17. Getreuer, P.: Chan-Vese Segmentation. Image Processing on Line 2012 (2012), doi:10.5201/ipol.2012.g-cv

    Google Scholar 

  18. Gómez, J., Patricio, M.A., García, J., Molina, J.M.: Communication in distributed tracking systems: an ontology-based approach to improve cooperation. Expert Systems 28(4), 288–305 (2011)

    CrossRef  Google Scholar 

  19. Griol, D., García-Herrero, J., Molina, J.M.: Combining heterogeneous inputs for the development of adaptive and multimodal interaction systems. Advances in Distributed Computing and Artificial Intelligence Journal 6, 37–53 (2013) ISSN 2255-2863

    Google Scholar 

  20. Haehnel, H.: Remote controlled flying robot platform. In: Third International Conference on Digital Information Management, ICDIM 2008, pp. 920–921. IEEE (November 2008)

    Google Scholar 

  21. Bajo, J., De Paz, J.F., Rodríguez, S., González, A.: Multi-agent system to monitor oceanic environments. Integrated Computer-Aided Engineering 17(2), 131–144 (2010)

    Google Scholar 

  22. Bajo, J., Corchado, J.M.: Evaluation and monitoring of the air-sea interaction using a CBR-Agents approach. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 50–62. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  23. J. C. G. R. R. A. B. a. J. M. V. M. Ángel Farfán: Game harvest characterisation of the mammals in Andalusia. Galemys 16(1), 41–59 (2004)

    Google Scholar 

  24. J. D. R. a. K. R. G. Felix A. Wichmann: Animal detection in natural scenes: Critical features revisited. Journal of Vision 10(4) (April 2010)

    Google Scholar 

  25. Anderson, J.I.J.M.: Tropical Soil Biology and Fertility. A Handbook of Methods. de CAB International, Wallingford (1993)

    Google Scholar 

  26. Gallego, J.I.R.: Caza y turismo cinegético como instrumentos. Anales de Geografía de la Universidad Complutense 30(2) (Octubre 2010)

    Google Scholar 

  27. Fraile, J.A., Bajo, J., Corchado, J.M., Abraham, A.: Applying wearable solutions in dependent environments. IEEE Transactions on Information Technology in Biomedicine 14(6), 1459–1467 (2011)

    CrossRef  Google Scholar 

  28. De Paz, J.F., Rodríguez, S., Bajo, J., Corchado, J.M.: Case-based reasoning as a decision support system for cancer diagnosis: A case study. International Journal of Hybrid Intelligent Systems 6(2), 97–110 (2009)

    Google Scholar 

  29. De Paz, J.F., Rodríguez, S., Bajo, J., Corchado, J.M.: Mathematical model for dynamic case-based planning. International Journal of Computer Mathematics 86(10-11), 1719–1730 (2009)

    CrossRef  MATH  Google Scholar 

  30. Corchado Rodríguez, J.M.: Redes Neuronales Artificiales: un enfoque práctico. Servicio de Publicacións da Universidade de Vigo, Vigo (2000)

    Google Scholar 

  31. Corchado, J.M., Lees, B.: Adaptation of cases for case based forecasting with neural network support. In: Soft Computing in Case Based Reasoning, pp. 293–319 (2001)

    Google Scholar 

  32. Corchado, J.M., Fyfe, C.: Unsupervised neural method for temperature forecasting. Artificial Intelligence in Engineering 13(4), 351–357 (1999)

    CrossRef  Google Scholar 

  33. Corchado, J.M., Aiken, J., Rees, N.: Artificial intelligence models for oceanographic forecasting. Plymouth Marine Laboratory (2001)

    Google Scholar 

  34. Corchado, J.M., Aiken, J.: Hybrid artificial intelligence methods in oceanographic forecast models. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 32(4), 307–313 (2002)

    CrossRef  Google Scholar 

  35. Corchado, J.M., Bajo, J., De Paz, J.F., Rodríguez, S.: An execution time neural-CBR guidance assistant. Neurocomputing 72(13), 2743–2753 (2009)

    CrossRef  Google Scholar 

  36. Corchado, J.M., De Paz, J.F., Rodríguez, S., Bajo, J.: Model of experts for decision support in the diagnosis of leukemia patients. Artificial Intelligence in Medicine 46(3), 179–200 (2009)

    CrossRef  Google Scholar 

  37. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998), doi:10.1109/5.726791

    CrossRef  Google Scholar 

  38. Parihk, M., Pately, M., Bhat, D.: Animal Detection Using Template Matching Algorithm. International Journal of Research in Modern Engineering and Emerging Technology 1(3) (2013)

    Google Scholar 

  39. Mahony, R., Kumar, V., Corke, P.: Multirotor aerial vehicles: Modeling, estimation, and control of quadrotor (2012)

    Google Scholar 

  40. Borrajo, M.L., Baruque, B., Corchado, E., Bajo, J., Corchado, J.M.: Hybrid neural intelligent system to predict business failure in small-to-medium-size enterprises. International Journal of Neural Systems 21(4), 277–296 (2011)

    CrossRef  Google Scholar 

  41. Ramli, H., Kuntjoro, W., Makhtar, A.K.: Advanced Autonomous Multirotor Response System. Applied Mechanics and Materials 393, 299–304 (2013)

    CrossRef  Google Scholar 

  42. Rodriguez, S., Julián, V., Bajo, J., Carrascosa, C., Botti, V., Corchado, J.M.: Agent-based virtual organization architecture. Engineering Applications of Artificial Intelligence 24(5), 895–910

    Google Scholar 

  43. Rodríguez, S., Pérez-Lancho, B., De Paz, J.F., Bajo, J., Corchado, J.M.: Ovamah: Multiagent-based adaptive virtual organizations. In: 12th International Conference on Information Fusion, FUSION 2009, pp. 990–997 (2009)

    Google Scholar 

  44. Rodríguez, S., de Paz, Y., Bajo, J., Corchado, J.M.: Social-based planning model for multiagent systems. Expert Systems with Applications 38(10), 13005–13023 (2011)

    CrossRef  Google Scholar 

  45. Macrofauna, S., Lavelle, P., Senapati, B., Barros, E.: Trees, Crops and Soil Fertility: Concepts and Research Methods, pp. 303–323 (2003)

    Google Scholar 

  46. Svanfeldt, M.: Design of the hardware platform for the flight control system in an unmanned aerial vehicle (Doctoral dissertation. Linköping) (2010)

    Google Scholar 

  47. Tretyakov, V., Surmann, H.: Hardware architecture of a four-rotor UAV for USAR/WSAR scenarios. In: Workshop Proceedings of SIMPAR 2008-International Conference on Simulation, Modeling and Programming for Autonomous Robots (2008)

    Google Scholar 

  48. Zato, C., Villarrubia, G., Sánchez, A., Bajo, J., Corchado, J.M.: PANGEA: A New Platform for Developing Virtual Organizations of Agents. International Journal of Artificial Intelligence TM 11(A13), 93–102 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pablo Chamoso .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Chamoso, P., Raveane, W., Parra, V., González, A. (2014). UAVs Applied to the Counting and Monitoring of Animals. In: Ramos, C., Novais, P., Nihan, C., Corchado Rodríguez, J. (eds) Ambient Intelligence - Software and Applications. Advances in Intelligent Systems and Computing, vol 291. Springer, Cham. https://doi.org/10.1007/978-3-319-07596-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-07596-9_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07595-2

  • Online ISBN: 978-3-319-07596-9

  • eBook Packages: EngineeringEngineering (R0)