| Форма представления | Тезисы и материалы конференций в зарубежных журналах и сборниках |
| Год публикации | 2021 |
| Язык | английский |
|
Магид Евгений Аркадьевич, автор
|
| Библиографическое описание на языке оригинала |
Myrzin V. Visual data processing framework for a skin-based human detection / Tsoy T., Bai Y., Svinin M., Magid E. // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 2021. - № 12998. - p 138-149. |
| Аннотация |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Ключевые слова |
Visual data processing, Skin segmentation, Feature extraction, Image classification, Mobile robot |
| Название журнала |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
|
| URL |
https://link.springer.com/chapter/10.1007/978-3-030-87725-5_12 |
| Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на эту карточку |
https://repository.kpfu.ru/?p_id=257407 |
| Файлы ресурса | |
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Полная запись метаданных  |
| Поле DC |
Значение |
Язык |
| dc.contributor.author |
Магид Евгений Аркадьевич |
ru_RU |
| dc.date.accessioned |
2021-01-01T00:00:00Z |
ru_RU |
| dc.date.available |
2021-01-01T00:00:00Z |
ru_RU |
| dc.date.issued |
2021 |
ru_RU |
| dc.identifier.citation |
Myrzin V. Visual data processing framework for a skin-based human detection / Tsoy T., Bai Y., Svinin M., Magid E. // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 2021. - № 12998. - p 138-149. |
ru_RU |
| dc.identifier.uri |
https://repository.kpfu.ru/?p_id=257407 |
ru_RU |
| dc.description.abstract |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
ru_RU |
| dc.description.abstract |
For a large variety of tasks autonomous robots require a robust visual data processing system. This paper presents a new human detection framework that combines rotation-invariant histogram of oriented gradients (RIHOG) features and binarized normed gradients (BING) pre-processing and skin segmentation. For experimental evaluation a new Human body dataset of over 60000 images was constructed using the Human-Parts dataset, the Simulated disaster victim dataset, and the Servosila Engineer robot dataset. Random, Liner SVM, Quadratic SVM, AdaBoost, and Random Forest approaches were compared using the Human body dataset. Experimental evaluation demonstrated an average precision of 90.4% for the Quadratic SVM model and showed the efficiency of RIHOG features as a descriptor for human detection tasks. |
ru_RU |
| dc.language.iso |
ru |
ru_RU |
| dc.subject |
Visual data processing |
ru_RU |
| dc.subject |
Skin segmentation |
ru_RU |
| dc.subject |
Feature extraction |
ru_RU |
| dc.subject |
Image classification |
ru_RU |
| dc.subject |
Mobile robot |
ru_RU |
| dc.title |
Visual data processing framework for a skin-based human detection |
ru_RU |
| dc.type |
Тезисы и материалы конференций в зарубежных журналах и сборниках |
ru_RU |
|
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