Neural network algorithm for predicting human speed based on computer vision and machine learning
The problem of increasing the accuracy of predicting human actions is an urgent task for various human-machine systems. The study examines the solution to the problem of predicting human speed using neural network algorithms, computer vision technologies, and machine learning. The formalization and...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2024/02/itmconf_hmmocs2023_03003.pdf |
| _version_ | 1797333995524980736 |
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| author | Obukhov Artem Teselkin Daniil Surkova Ekaterina Komissarov Artem Shilcin Maxim |
| author_facet | Obukhov Artem Teselkin Daniil Surkova Ekaterina Komissarov Artem Shilcin Maxim |
| author_sort | Obukhov Artem |
| collection | DOAJ |
| description | The problem of increasing the accuracy of predicting human actions is an urgent task for various human-machine systems. The study examines the solution to the problem of predicting human speed using neural network algorithms, computer vision technologies, and machine learning. The formalization and software implementation of a neural network speed prediction algorithm are presented. To solve the problems of determining the current speed and predicting the upcoming positions of the human body depending on the dynamics of its movement, a comparison of various machine learning models was carried out. The RandomForestRegressor algorithm showed the best position prediction accuracy. The best determination of the current speed was demonstrated by dense multilayer neural networks. The experiment revealed that when predicting a person's position at an interval of 0.6 seconds, his speed is determined with an accuracy of more than 90%. The results obtained can be used to implement neural network algorithms for controlling human-machine systems. |
| first_indexed | 2024-03-08T08:13:18Z |
| format | Article |
| id | doaj.art-3a34c77bc63a4c648cee5f97fc7faa4e |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-03-08T08:13:18Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-3a34c77bc63a4c648cee5f97fc7faa4e2024-02-02T08:04:05ZengEDP SciencesITM Web of Conferences2271-20972024-01-01590300310.1051/itmconf/20245903003itmconf_hmmocs2023_03003Neural network algorithm for predicting human speed based on computer vision and machine learningObukhov Artem0Teselkin Daniil1Surkova Ekaterina2Komissarov Artem3Shilcin Maxim4Department of Automated Systems of Decision-Making Support, Tambov State Technical UniversityDepartment of Automated Systems of Decision-Making Support, Tambov State Technical UniversityDepartment of Automated Systems of Decision-Making Support, Tambov State Technical UniversityDepartment of Mechatronics and Technological Measurements, Tambov State Technical UniversityDepartment of Automated Systems of Decision-Making Support, Tambov State Technical UniversityThe problem of increasing the accuracy of predicting human actions is an urgent task for various human-machine systems. The study examines the solution to the problem of predicting human speed using neural network algorithms, computer vision technologies, and machine learning. The formalization and software implementation of a neural network speed prediction algorithm are presented. To solve the problems of determining the current speed and predicting the upcoming positions of the human body depending on the dynamics of its movement, a comparison of various machine learning models was carried out. The RandomForestRegressor algorithm showed the best position prediction accuracy. The best determination of the current speed was demonstrated by dense multilayer neural networks. The experiment revealed that when predicting a person's position at an interval of 0.6 seconds, his speed is determined with an accuracy of more than 90%. The results obtained can be used to implement neural network algorithms for controlling human-machine systems.https://www.itm-conferences.org/articles/itmconf/pdf/2024/02/itmconf_hmmocs2023_03003.pdf |
| spellingShingle | Obukhov Artem Teselkin Daniil Surkova Ekaterina Komissarov Artem Shilcin Maxim Neural network algorithm for predicting human speed based on computer vision and machine learning ITM Web of Conferences |
| title | Neural network algorithm for predicting human speed based on computer vision and machine learning |
| title_full | Neural network algorithm for predicting human speed based on computer vision and machine learning |
| title_fullStr | Neural network algorithm for predicting human speed based on computer vision and machine learning |
| title_full_unstemmed | Neural network algorithm for predicting human speed based on computer vision and machine learning |
| title_short | Neural network algorithm for predicting human speed based on computer vision and machine learning |
| title_sort | neural network algorithm for predicting human speed based on computer vision and machine learning |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2024/02/itmconf_hmmocs2023_03003.pdf |
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