An Intention Understanding Algorithm Based on Multimodal Information Fusion
This paper proposes an intention understanding algorithm (KDI) based on an elderly service robot, which combines Neural Network with a seminaive Bayesian classifier to infer user’s intention. KDI algorithm uses CNN to analyze gesture and action information, and YOLOV3 is used for object detection to...
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Hindawi Limited
2021
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oai:doaj.org-article:6ebbeebb374943e3aa0ede04bbd3f2162021-11-29T00:56:24ZAn Intention Understanding Algorithm Based on Multimodal Information Fusion1875-919X10.1155/2021/8354015https://doaj.org/article/6ebbeebb374943e3aa0ede04bbd3f2162021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8354015https://doaj.org/toc/1875-919XThis paper proposes an intention understanding algorithm (KDI) based on an elderly service robot, which combines Neural Network with a seminaive Bayesian classifier to infer user’s intention. KDI algorithm uses CNN to analyze gesture and action information, and YOLOV3 is used for object detection to provide scene information. Then, we enter them into a seminaive Bayesian classifier and set key properties as super parent to enhance its contribution to an intent, realizing intention understanding based on prior knowledge. In addition, we introduce the actual distance between the users and objects and give each object a different purpose to implement intent understanding based on object-user distance. The two methods are combined to enhance the intention understanding. The main contributions of this paper are as follows: (1) an intention reasoning model (KDI) is proposed based on prior knowledge and distance, which combines Neural Network with seminaive Bayesian classifier. (2) A set of robot accompanying systems based on the robot is formed, which is applied in the elderly service scene.Shaosong DouZhiquan FengJinglan TianXue FanYa HouXin ZhangHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021) |
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Computer software QA76.75-76.765 |
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Computer software QA76.75-76.765 Shaosong Dou Zhiquan Feng Jinglan Tian Xue Fan Ya Hou Xin Zhang An Intention Understanding Algorithm Based on Multimodal Information Fusion |
description |
This paper proposes an intention understanding algorithm (KDI) based on an elderly service robot, which combines Neural Network with a seminaive Bayesian classifier to infer user’s intention. KDI algorithm uses CNN to analyze gesture and action information, and YOLOV3 is used for object detection to provide scene information. Then, we enter them into a seminaive Bayesian classifier and set key properties as super parent to enhance its contribution to an intent, realizing intention understanding based on prior knowledge. In addition, we introduce the actual distance between the users and objects and give each object a different purpose to implement intent understanding based on object-user distance. The two methods are combined to enhance the intention understanding. The main contributions of this paper are as follows: (1) an intention reasoning model (KDI) is proposed based on prior knowledge and distance, which combines Neural Network with seminaive Bayesian classifier. (2) A set of robot accompanying systems based on the robot is formed, which is applied in the elderly service scene. |
format |
article |
author |
Shaosong Dou Zhiquan Feng Jinglan Tian Xue Fan Ya Hou Xin Zhang |
author_facet |
Shaosong Dou Zhiquan Feng Jinglan Tian Xue Fan Ya Hou Xin Zhang |
author_sort |
Shaosong Dou |
title |
An Intention Understanding Algorithm Based on Multimodal Information Fusion |
title_short |
An Intention Understanding Algorithm Based on Multimodal Information Fusion |
title_full |
An Intention Understanding Algorithm Based on Multimodal Information Fusion |
title_fullStr |
An Intention Understanding Algorithm Based on Multimodal Information Fusion |
title_full_unstemmed |
An Intention Understanding Algorithm Based on Multimodal Information Fusion |
title_sort |
intention understanding algorithm based on multimodal information fusion |
publisher |
Hindawi Limited |
publishDate |
2021 |
url |
https://doaj.org/article/6ebbeebb374943e3aa0ede04bbd3f216 |
work_keys_str_mv |
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_version_ |
1718407713719844864 |