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Development of a Pets' Body Movement Recognition Technique Using Deep Neural Networks
Author(s) -
Yeh ChengYu,
Lai HsiangYueh,
Huang HungHsun
Publication year - 2021
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.23341
Subject(s) - artificial intelligence , movement (music) , artificial neural network , computer science , computer vision , raising (metalworking) , psychology , pattern recognition (psychology) , engineering , art , aesthetics , mechanical engineering
This paper presents a deep neural network‐based technique to recognize pets' body movements. The motivation to develop the technique arises from the fact that there are an increasing number of animal lovers, say dog or cat lovers, who spend a tremendous amount of effort to take care of their beloved pets, and even to capture the pets' images in daily life and at particular memorable moments for sharing among friends. For illustrative purposes, the recognition model was trained and then tested using a number of cats' images. As it turned out, the model well recognized three body movements: eating, tail raising and yawn, with a recognition accuracy up to 99.45%. Using this recognition technique, pets' images can be captured automatically once specific movements are detected, and job as a pet photographer can be made easy accordingly. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.