Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap data
This paper presents a feature selection comparison oriented to human action recognition only with the kinematic features of skeleton representation. For this purpose, three relevance methods are used to rank the contribution of kinematic features for classifying an action is employed. Particularly,...
- Autores:
-
Pulgarín Giraldo, Juan Diego
Alvarez Meza, Andres Marino
Ruales Tores, A. A.
Castellanos Dominguez, German
- Tipo de recurso:
- Part of book
- Fecha de publicación:
- 2017
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/11615
- Acceso en línea:
- http://hdl.handle.net/10614/11615
https://link.springer.com/chapter/10.1007/978-3-319-59740-9_23
https://link.springer.com/content/pdf/10.1007%2F978-3-319-59740-9.pdf
https://doi.org/10.1007/978-3-319-59773-7_51
- Palabra clave:
- Center kernel alignment
Feature selection
Human motion
Kinematics
Relevance
ReliefF
Motion capture data
Principal Component Analysis
Kinematics
Human beings--Attitude and movement
Cinemática
Actitud y movimiento del hombre
Motion
Mechanical movements
Movimiento
Movimientos mecánicos
- Rights
- openAccess
- License
- Derechos Reservados - Universidad Autónoma de Occidente