Weighted Classification of Machine Learning to Recognize Human Activities
This paper presents a new method to recognize human activities based on weighted classification for the features extracted by human body. Towards this end, new features depend on weight taken from image or video used in proposed descriptor. Human pose plays an important role in extracted features; t...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5593916 |
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| _version_ | 1850170320284024832 |
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| author | Guorong Wu Zichen Liu Xuhui Chen |
| author_facet | Guorong Wu Zichen Liu Xuhui Chen |
| author_sort | Guorong Wu |
| collection | DOAJ |
| description | This paper presents a new method to recognize human activities based on weighted classification for the features extracted by human body. Towards this end, new features depend on weight taken from image or video used in proposed descriptor. Human pose plays an important role in extracted features; then these features are used as the weight input with classifier. We use machine learning during two steps of training and testing images of standard dataset that can be used during benchmarking the system. Unlike previous methods that need size or length of shapes mainly to represent the cues when machine learning is used to recognize human activities, accurate experimental results coming from appropriate segments of the human body proved the worthiness of proposed method. Twelve activities are used in challenging of availability comparison with dataset to demonstrate our method. The results show that we achieved 87.3% in training set, while in testing set, we achieved 94% in terms of precision. |
| format | Article |
| id | doaj-art-a2fbc88795544571ad1c5773aef4ebf7 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a2fbc88795544571ad1c5773aef4ebf72025-08-20T02:20:30ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55939165593916Weighted Classification of Machine Learning to Recognize Human ActivitiesGuorong Wu0Zichen Liu1Xuhui Chen2School of Art and Design, Nanchang University, Nanchang, ChinaSchool of Art and Design, Nanchang University, Nanchang, ChinaSchool of Art and Design, Nanchang University, Nanchang, ChinaThis paper presents a new method to recognize human activities based on weighted classification for the features extracted by human body. Towards this end, new features depend on weight taken from image or video used in proposed descriptor. Human pose plays an important role in extracted features; then these features are used as the weight input with classifier. We use machine learning during two steps of training and testing images of standard dataset that can be used during benchmarking the system. Unlike previous methods that need size or length of shapes mainly to represent the cues when machine learning is used to recognize human activities, accurate experimental results coming from appropriate segments of the human body proved the worthiness of proposed method. Twelve activities are used in challenging of availability comparison with dataset to demonstrate our method. The results show that we achieved 87.3% in training set, while in testing set, we achieved 94% in terms of precision.http://dx.doi.org/10.1155/2021/5593916 |
| spellingShingle | Guorong Wu Zichen Liu Xuhui Chen Weighted Classification of Machine Learning to Recognize Human Activities Complexity |
| title | Weighted Classification of Machine Learning to Recognize Human Activities |
| title_full | Weighted Classification of Machine Learning to Recognize Human Activities |
| title_fullStr | Weighted Classification of Machine Learning to Recognize Human Activities |
| title_full_unstemmed | Weighted Classification of Machine Learning to Recognize Human Activities |
| title_short | Weighted Classification of Machine Learning to Recognize Human Activities |
| title_sort | weighted classification of machine learning to recognize human activities |
| url | http://dx.doi.org/10.1155/2021/5593916 |
| work_keys_str_mv | AT guorongwu weightedclassificationofmachinelearningtorecognizehumanactivities AT zichenliu weightedclassificationofmachinelearningtorecognizehumanactivities AT xuhuichen weightedclassificationofmachinelearningtorecognizehumanactivities |