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Motion History Images for Action Recognition and Understanding [electronic resource] / by Md. Atiqur Rahman Ahad.

Por: Tipo de material: TextoTextoSeries SpringerBriefs in Computer Science | SpringerBriefs in Computer ScienceEditor: London : Springer London : Imprint: Springer, 2013Descripción: XVI, 116 p. 34 illus. online resourceTipo de contenido:
  • text
Tipo de medio:
  • computer
Tipo de soporte:
  • online resource
ISBN:
  • 9781447147305
Trabajos contenidos:
  • SpringerLink (Online service)
Tema(s): Formatos físicos adicionales: Sin títuloClasificación CDD:
  • 006.4 23
Clasificación LoC:
  • Q337.5
  • TK7882.P3
Recursos en línea:
Contenidos:
Springer eBooksResumen: 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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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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