ADAPTIVE VISION AI
Abstract. As of today, computer vision systems are continuously developing and systematically improving. Machines see visual content in the form of numbers, in which each pixel represents its own piece of information. Computer vision, as a component of artificial intelligence, allows machines to see...
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| Format: | Article |
| Language: | English |
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Odessa National Academy of Food Technologies
2024-12-01
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| Series: | Автоматизация технологических и бизнес-процессов |
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| Online Access: | https://journals.ontu.edu.ua/index.php/atbp/article/view/3013 |
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| author | V. Vodyanitskyi V. Yuskovych-Zhukovska |
| author_facet | V. Vodyanitskyi V. Yuskovych-Zhukovska |
| author_sort | V. Vodyanitskyi |
| collection | DOAJ |
| description | Abstract. As of today, computer vision systems are continuously developing and systematically improving. Machines see visual content in the form of numbers, in which each pixel represents its own piece of information. Computer vision, as a component of artificial intelligence, allows machines to see, observe and understand everything. It enables computer systems to obtain useful information from digital images, video, visual data and perform programmed actions. Computer vision technologies rely on pattern recognition, machine learning, and neural networks to allow computers to break down images, interpret data, and identify features. Tracking moving objects and their identification is a difficult task, as it requires the accuracy of pattern recognition. An untrained computer vision algorithm is unable to understand the relationship between the shapes in the image and the objects. Therefore, the algorithm must be trained. The paper considers models that are trained on a high-performance computing cluster with GPU support. The developed open source software allows detection, tracking and recognition of blurry moving objects with the help of artificial intelligence that adapts to any video camera. A significant increase in accuracy is achieved thanks to machine learning. |
| format | Article |
| id | doaj-art-a20199852eae48d79e1973e435a7885e |
| institution | Kabale University |
| issn | 2312-3125 2312-931X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Odessa National Academy of Food Technologies |
| record_format | Article |
| series | Автоматизация технологических и бизнес-процессов |
| spelling | doaj-art-a20199852eae48d79e1973e435a7885e2025-01-27T15:58:25ZengOdessa National Academy of Food TechnologiesАвтоматизация технологических и бизнес-процессов2312-31252312-931X2024-12-01164738110.15673/atbp.v16i4.30133013ADAPTIVE VISION AIV. Vodyanitskyi0V. Yuskovych-Zhukovska1Private Higher Education Establishment “Academician Stepan Demianchuk International University of Economics and Humanities”, Rivne, UkrainePrivate Higher Education Establishment “Academician Stepan Demianchuk International University of Economics and Humanities”, Rivne, UkraineAbstract. As of today, computer vision systems are continuously developing and systematically improving. Machines see visual content in the form of numbers, in which each pixel represents its own piece of information. Computer vision, as a component of artificial intelligence, allows machines to see, observe and understand everything. It enables computer systems to obtain useful information from digital images, video, visual data and perform programmed actions. Computer vision technologies rely on pattern recognition, machine learning, and neural networks to allow computers to break down images, interpret data, and identify features. Tracking moving objects and their identification is a difficult task, as it requires the accuracy of pattern recognition. An untrained computer vision algorithm is unable to understand the relationship between the shapes in the image and the objects. Therefore, the algorithm must be trained. The paper considers models that are trained on a high-performance computing cluster with GPU support. The developed open source software allows detection, tracking and recognition of blurry moving objects with the help of artificial intelligence that adapts to any video camera. A significant increase in accuracy is achieved thanks to machine learning.https://journals.ontu.edu.ua/index.php/atbp/article/view/3013artificial intelligencepattern recognitioncomputer visionmachine learningneural networksfuzzy moving objects |
| spellingShingle | V. Vodyanitskyi V. Yuskovych-Zhukovska ADAPTIVE VISION AI Автоматизация технологических и бизнес-процессов artificial intelligence pattern recognition computer vision machine learning neural networks fuzzy moving objects |
| title | ADAPTIVE VISION AI |
| title_full | ADAPTIVE VISION AI |
| title_fullStr | ADAPTIVE VISION AI |
| title_full_unstemmed | ADAPTIVE VISION AI |
| title_short | ADAPTIVE VISION AI |
| title_sort | adaptive vision ai |
| topic | artificial intelligence pattern recognition computer vision machine learning neural networks fuzzy moving objects |
| url | https://journals.ontu.edu.ua/index.php/atbp/article/view/3013 |
| work_keys_str_mv | AT vvodyanitskyi adaptivevisionai AT vyuskovychzhukovska adaptivevisionai |