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, the method with the best results includes the supervised information regarding the action to find out a relevant set of features, encoding the most discriminative information. Experimental results are obtained on a well-known public data (MSR Action3D). Results are encouraging to use kernel theory methods to get better kinematic feature selection for each action with a good generalization indistinct to the subject

• 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