Performance Evaluation of Neural Networks for Animal Behaviors Classification: Horse Gaits Case Study
| Autor: | Daniel Gutierrez-Galan, Antonio Rios-Navarro, Juan Pedro Dominguez-Morales, Ricardo Tapiador-Morales, C. Martín-Cañal, Elena Cerezuela-Escudero, Alejandro Linares-Barranco |
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| Přispěvatelé: | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Sevilla. TEP-108: Robótica y Tecnología de Computadores Aplicada a la Rehabilitación |
| Rok vydání: | 2016 |
| Předmět: |
Sensor fusion
Artificial neural network business.industry Computer science 020208 electrical & electronic engineering 02 engineering and technology Inertial sensors Machine learning computer.software_genre Perceptron Task (project management) Feed-forward neural network Multi-Layer Perceptron Inertial measurement unit Multilayer perceptron Pattern recognition Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering Feedforward neural network 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | idUS. Depósito de Investigación de la Universidad de Sevilla instname Distributed Computing and Artificial Intelligence, 13th International Conference ISBN: 9783319401614 DCAI |
| Popis: | The study and monitoring of wildlife has always been a subject of great interest. Studying the behavior of wildlife animals is a very complex task due to the difficulties to track them and classify their behaviors through the collected sensory information. Novel technology allows designing low cost systems that facilitate these tasks. There are currently some commercial solutions to this problem; however, it is not possible to obtain a highly accurate classification due to the lack of gathered information. In this work, we propose an animal behavior recognition, classification and monitoring system based on a smart collar device provided with inertial sensors and a feed-forward neural network or Multi-Layer Perceptron (MLP) to classify the possible animal behavior based on the collected sensory information. Experimental results over horse gaits case study show that the recognition system achieves an accuracy of up to 95.6%. Junta de Andalucía P12-TIC-1300 |
| Databáze: | OpenAIRE |
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