Motion History Images for Action Recognition and Understanding / by Md. Atiqur Rahman Ahad.

Por:

Ahad, Md. Atiqur Rahman [autor]

Colaborador(es):

SpringerLink (Servicio en línea)

Tipo de material: Texto

TextoSeries SpringerBriefs in Computer ScienceEditor: London : Springer London : Imprint: Springer, 2013Descripción: xvI, 116 páginas 34 ilustraciones recurso en líneaTipo de contenido:

texto

Tipo de medio:

computadora

Tipo de portador:

recurso en línea

ISBN:

9781447147305

Formatos físicos adicionales: Edición impresa:: Sin títuloClasificación LoC:

Q337.5

Recursos en línea:

Conectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)

Contenidos:

Introduction -- Action Representation -- Motion History Image -- Action Datasets and MHI.

Resumen: Human action analysis and recognition is a relatively mature field, yet one which is often not well understood by students and researchers. The large number of possible variations in human motion and appearance, camera viewpoint, and environment, present considerable challenges. Some important and common problems remain unsolved by the computer vision community. However, many valuable approaches have been proposed over the past decade, including the motion history image (MHI) method. This method has received significant attention, as it offers greater robustness and performance than other techniques. This work presents a comprehensive review of these state-of-the-art approaches and their applications, with a particular focus on the MHI method and its variants.

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Springer eBooks

Introduction -- Action Representation -- Motion History Image -- Action Datasets and MHI.

Human action analysis and recognition is a relatively mature field, yet one which is often not well understood by students and researchers. The large number of possible variations in human motion and appearance, camera viewpoint, and environment, present considerable challenges. Some important and common problems remain unsolved by the computer vision community. However, many valuable approaches have been proposed over the past decade, including the motion history image (MHI) method. This method has received significant attention, as it offers greater robustness and performance than other techniques. This work presents a comprehensive review of these state-of-the-art approaches and their applications, with a particular focus on the MHI method and its variants.

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