An Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ Behaviors

Dogs and cats tend to show their conditions and desires through their behaviors. In companion animal behavior recognition, behavior data obtained by attaching a wearable device or sensor to a dog’s body are mostly used. However, differences occur in the output values of the sensor when the dog moves...

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Autores principales:	Hye-Jin Lee, Sun-Young Ihm, So-Hyun Park, Young-Ho Park
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence dog behaviors multi class classification tensor fusion Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/62b75da753794be2b0119ae084edaf3d
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id	oai:doaj.org-article:62b75da753794be2b0119ae084edaf3d
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spelling	oai:doaj.org-article:62b75da753794be2b0119ae084edaf3d2021-11-25T16:43:21ZAn Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ Behaviors10.3390/app1122110502076-3417https://doaj.org/article/62b75da753794be2b0119ae084edaf3d2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/11050https://doaj.org/toc/2076-3417Dogs and cats tend to show their conditions and desires through their behaviors. In companion animal behavior recognition, behavior data obtained by attaching a wearable device or sensor to a dog’s body are mostly used. However, differences occur in the output values of the sensor when the dog moves violently. A tightly coupled RGB time tensor network (TRT-Net) is proposed that minimizes the loss of spatiotemporal information by reflecting the three components (x-, y-, and z-axes) of the skeleton sequences in the corresponding three channels (red, green, and blue) for the behavioral classification of dogs. This paper introduces the YouTube-C7B dataset consisting of dog behaviors in various environments. Based on a method that visualizes the Conv-layer filters in analyzable feature maps, we add reliability to the results derived by the model. We can identify the joint parts, i.e., those represented as rows of input images showing behaviors, learned by the proposed model mainly for making decisions. Finally, the performance of the proposed method is compared to those of the LSTM, GRU, and RNN models. The experimental results demonstrate that the proposed TRT-Net method classifies dog behaviors more effectively, with improved accuracy and F1 scores of 7.9% and 7.3% over conventional models.Hye-Jin LeeSun-Young IhmSo-Hyun ParkYoung-Ho ParkMDPI AGarticleartificial intelligencedog behaviorsmulti class classificationtensor fusionTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 11050, p 11050 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence dog behaviors multi class classification tensor fusion Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	artificial intelligence dog behaviors multi class classification tensor fusion Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Hye-Jin Lee Sun-Young Ihm So-Hyun Park Young-Ho Park An Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ Behaviors
description	Dogs and cats tend to show their conditions and desires through their behaviors. In companion animal behavior recognition, behavior data obtained by attaching a wearable device or sensor to a dog’s body are mostly used. However, differences occur in the output values of the sensor when the dog moves violently. A tightly coupled RGB time tensor network (TRT-Net) is proposed that minimizes the loss of spatiotemporal information by reflecting the three components (x-, y-, and z-axes) of the skeleton sequences in the corresponding three channels (red, green, and blue) for the behavioral classification of dogs. This paper introduces the YouTube-C7B dataset consisting of dog behaviors in various environments. Based on a method that visualizes the Conv-layer filters in analyzable feature maps, we add reliability to the results derived by the model. We can identify the joint parts, i.e., those represented as rows of input images showing behaviors, learned by the proposed model mainly for making decisions. Finally, the performance of the proposed method is compared to those of the LSTM, GRU, and RNN models. The experimental results demonstrate that the proposed TRT-Net method classifies dog behaviors more effectively, with improved accuracy and F1 scores of 7.9% and 7.3% over conventional models.
format	article
author	Hye-Jin Lee Sun-Young Ihm So-Hyun Park Young-Ho Park
author_facet	Hye-Jin Lee Sun-Young Ihm So-Hyun Park Young-Ho Park
author_sort	Hye-Jin Lee
title	An Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ Behaviors
title_short	An Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ Behaviors
title_full	An Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ Behaviors
title_fullStr	An Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ Behaviors
title_full_unstemmed	An Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ Behaviors
title_sort	analytic method for improving the reliability of models based on a histogram for prediction of companion dogs’ behaviors
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/62b75da753794be2b0119ae084edaf3d
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An Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ Behaviors

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