UAVs Applied to the Counting and Monitoring of Animals
Conference paper
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 detectionPreview
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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)CrossRefGoogle 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), 5486–5499Google 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefMathSciNetGoogle 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/BF00344251CrossRefzbMATHGoogle 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)CrossRefGoogle 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-cvGoogle 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)CrossRefGoogle 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 2255-2863Google 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefzbMATHGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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:10.1109/5.726791CrossRefGoogle 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)CrossRefGoogle Scholar
- 41.Ramli, H., Kuntjoro, W., Makhtar, A.K.: Advanced Autonomous Multirotor Response System. Applied Mechanics and Materials 393, 299–304 (2013)CrossRefGoogle 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), 895–910Google 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)CrossRefGoogle 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
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