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,...

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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