Identity statement area
Reference TypeConference Proceedings
Last Update2018:
Metadata Last Update2020: administrator
Citation KeyCejnogJrCampElui:2018:InHaTh
TitleInjured hand therapy evaluation using hand tracking
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 04
Number of Files1
Size1761 KiB
Context area
Author1 Cejnog, Luciano Walenty Xavier
2 Jr. , Roberto Marcondes Cesar
3 Campos, Teófilo de
4 Elui, Valéria Meirelles Carril
Affiliation1 Departamento de Ciência da Computação, IME/USP
2 Departamento de Ciência da Computação, IME/USP
3 Departamento de Ciência da Computação, Universidade de Brasília
4 Departamento de Terapia Ocupacional, FMUSP Ribeirão Preto
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
History2018-10-26 18:48:06 :: -> administrator ::
2020-02-20 22:06:51 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Tertiary TypeWork in Progress
Keywordshand tracking, hand pose estimation, computer vision, depth images.
AbstractHand tracking is a challenging problem in computer vision that has recently gained relevance with the development of cheap consumer-level depth cameras and virtual reality devices. The objective is to identify a hand model in a scene and track the model accurately in a sequence of frames. The main proposal of this project is the development of a framework for hand tracking and gesture analysis, using a 3D model able to express different patterns of hand pose. Methods for data acquisition, learning model parameters, hand tracking/detection in video sequences and movement analysis will be developed. Here we describe the formation of the dataset and the first tests with hand pose estimation methods. Future steps include the development of hand detection, pose estimation and tracking methods based on state-of-art, as well as the assessment of movement quantities using the joint angles from the skeletons estimated by the pose estimation methods.
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Target FileSIBGRAPI_WIP_2018(3).pdf
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Next Higher Units8JMKD3MGPAW/3RPADUS
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