Ponencia
Semi-wildlife gait patterns classification using Statistical Methods and Artificial Neural Networks
Autor/es | Gutiérrez Galán, Daniel
Domínguez Morales, Juan Pedro Miró Amarante, María Lourdes Gómez Rodríguez, Francisco de Asís Domínguez Morales, Manuel Jesús Rivas Pérez, Manuel Jiménez Fernández, Ángel Francisco Linares Barranco, Alejandro |
Departamento | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores |
Fecha de publicación | 2017 |
Fecha de depósito | 2019-07-09 |
Publicado en |
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ISBN/ISSN | 978-1-5090-6182-2 2161-4407 |
Resumen | Several studies have focused on classifying behavioral
patterns in wildlife and captive species to monitor their
activities and so to understanding the interactions of animals
and control their welfare, for biological ... Several studies have focused on classifying behavioral patterns in wildlife and captive species to monitor their activities and so to understanding the interactions of animals and control their welfare, for biological research or commercial purposes. The use of pattern recognition techniques, statistical methods and Overall Dynamic Body Acceleration (ODBA) are well known for animal behavior recognition tasks. The reconfigurability and scalability of these methods are not trivial, since a new study has to be done when changing any of the configuration parameters. In recent years, the use of Artificial Neural Networks (ANN) has increased for this purpose due to the fact that they can be easily adapted when new animals or patterns are required. In this context, a comparative study between a theoretical research is presented, where statistical and spectral analyses were performed and an embedded implementation of an ANN on a smart collar device was placed on semi-wild animals. This system is part of a project whose main aim is to monitor wildlife in real time using a wireless sensor network infrastructure. Different classifiers were tested and compared for three different horse gaits. Experimental results in a real time scenario achieved an accuracy of up to 90.7%, proving the efficiency of the embedded ANN implementation. |
Identificador del proyecto | P12-TIC-1300
TEC2016-77785-P |
Cita | Gutiérrez Galán, D., Domínguez Morales, J.P., Miró Amarante, M.L., Gómez Rodríguez, F.d.A., Domínguez Morales, M.J., Rivas Pérez, M.,...,Linares Barranco, A. (2017). Semi-wildlife gait patterns classification using Statistical Methods and Artificial Neural Networks. En IJCNN 2017 : International Joint Conference on Neural Networks (4036-4043), Anchorage, USA: IEEE Computer Society. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Semi-wildlife gait patterns.pdf | 621.1Kb | [PDF] | Ver/ | |