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Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes

Recent research has shown that the ubiquitous use of cameras and voice monitoring equipment in a home environment can raise privacy concerns and affect human mental health. This can be a major obstacle to the deployment of smart home systems for elderly or disabled care. This study uses a social rob...

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Autores principales: Yang, Guanci, Yang, Jing, Sheng, Weihua, Junior, Francisco Erivaldo Fernandes, Li, Shaobo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5982546/
https://www.ncbi.nlm.nih.gov/pubmed/29757211
http://dx.doi.org/10.3390/s18051530
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author Yang, Guanci
Yang, Jing
Sheng, Weihua
Junior, Francisco Erivaldo Fernandes
Li, Shaobo
author_facet Yang, Guanci
Yang, Jing
Sheng, Weihua
Junior, Francisco Erivaldo Fernandes
Li, Shaobo
author_sort Yang, Guanci
collection PubMed
description Recent research has shown that the ubiquitous use of cameras and voice monitoring equipment in a home environment can raise privacy concerns and affect human mental health. This can be a major obstacle to the deployment of smart home systems for elderly or disabled care. This study uses a social robot to detect embarrassing situations. Firstly, we designed an improved neural network structure based on the You Only Look Once (YOLO) model to obtain feature information. By focusing on reducing area redundancy and computation time, we proposed a bounding-box merging algorithm based on region proposal networks (B-RPN), to merge the areas that have similar features and determine the borders of the bounding box. Thereafter, we designed a feature extraction algorithm based on our improved YOLO and B-RPN, called F-YOLO, for our training datasets, and then proposed a real-time object detection algorithm based on F-YOLO (RODA-FY). We implemented RODA-FY and compared models on our MAT social robot. Secondly, we considered six types of situations in smart homes, and developed training and validation datasets, containing 2580 and 360 images, respectively. Meanwhile, we designed three types of experiments with four types of test datasets composed of 960 sample images. Thirdly, we analyzed how a different number of training iterations affects our prediction estimation, and then we explored the relationship between recognition accuracy and learning rates. Our results show that our proposed privacy detection system can recognize designed situations in the smart home with an acceptable recognition accuracy of 94.48%. Finally, we compared the results among RODA-FY, Inception V3, and YOLO, which indicate that our proposed RODA-FY outperforms the other comparison models in recognition accuracy.
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spelling pubmed-59825462018-06-05 Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes Yang, Guanci Yang, Jing Sheng, Weihua Junior, Francisco Erivaldo Fernandes Li, Shaobo Sensors (Basel) Article Recent research has shown that the ubiquitous use of cameras and voice monitoring equipment in a home environment can raise privacy concerns and affect human mental health. This can be a major obstacle to the deployment of smart home systems for elderly or disabled care. This study uses a social robot to detect embarrassing situations. Firstly, we designed an improved neural network structure based on the You Only Look Once (YOLO) model to obtain feature information. By focusing on reducing area redundancy and computation time, we proposed a bounding-box merging algorithm based on region proposal networks (B-RPN), to merge the areas that have similar features and determine the borders of the bounding box. Thereafter, we designed a feature extraction algorithm based on our improved YOLO and B-RPN, called F-YOLO, for our training datasets, and then proposed a real-time object detection algorithm based on F-YOLO (RODA-FY). We implemented RODA-FY and compared models on our MAT social robot. Secondly, we considered six types of situations in smart homes, and developed training and validation datasets, containing 2580 and 360 images, respectively. Meanwhile, we designed three types of experiments with four types of test datasets composed of 960 sample images. Thirdly, we analyzed how a different number of training iterations affects our prediction estimation, and then we explored the relationship between recognition accuracy and learning rates. Our results show that our proposed privacy detection system can recognize designed situations in the smart home with an acceptable recognition accuracy of 94.48%. Finally, we compared the results among RODA-FY, Inception V3, and YOLO, which indicate that our proposed RODA-FY outperforms the other comparison models in recognition accuracy. MDPI 2018-05-10 /pmc/articles/PMC5982546/ /pubmed/29757211 http://dx.doi.org/10.3390/s18051530 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, Guanci
Yang, Jing
Sheng, Weihua
Junior, Francisco Erivaldo Fernandes
Li, Shaobo
Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes
title Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes
title_full Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes
title_fullStr Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes
title_full_unstemmed Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes
title_short Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes
title_sort convolutional neural network-based embarrassing situation detection under camera for social robot in smart homes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5982546/
https://www.ncbi.nlm.nih.gov/pubmed/29757211
http://dx.doi.org/10.3390/s18051530
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