KFU. Card publication. Visual data processing framework for a skin-based human detection

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

VISUAL DATA PROCESSING FRAMEWORK FOR A SKIN-BASED HUMAN DETECTION

Form of presentation

Conference proceedings in international journals and collections

Year of publication

2021

Язык

английский

Authors, employees of KFU

Magid Evgeniy Arkadevich, author

Bibliographic description in the original language

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.

Annotation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Keywords

Visual data processing, Skin segmentation, Feature extraction, Image classification, Mobile robot

The name of the journal

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

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=257407&p_lang=2

Resource files

File name	Size (MB)	Format
F_2021_Visual_Data_Processing_Framework_for_a_Skin_Based__uman_Detection.pdf	2,02	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Magid Evgeniy Arkadevich	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/eng/?p_id=257407&p_lang=2	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	Conference proceedings in international journals and collections	ru_RU