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Motion History Images for Action Recognition and Understanding

  • Book
  • © 2013

Overview

  • Examines the state-of-the-art motion history image (MHI) method
  • Suitable for both undergraduate and graduate students familiar with basic image processing, and for researchers involved in human action and activity representation and recognition
  • The author has more than six years’ experience in this field
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)

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About this book

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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Keywords

Table of contents (4 chapters)

Authors and Affiliations

  • Faculty of Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Md. Atiqur Rahman Ahad

Bibliographic Information

  • Book Title: Motion History Images for Action Recognition and Understanding

  • Authors: Md. Atiqur Rahman Ahad

  • Series Title: SpringerBriefs in Computer Science

  • DOI: https://doi.org/10.1007/978-1-4471-4730-5

  • Publisher: Springer London

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Md. Atiqur Rahman Ahad 2013

  • Softcover ISBN: 978-1-4471-4729-9Published: 20 December 2012

  • eBook ISBN: 978-1-4471-4730-5Published: 28 December 2012

  • Series ISSN: 2191-5768

  • Series E-ISSN: 2191-5776

  • Edition Number: 1

  • Number of Pages: XVI, 116

  • Number of Illustrations: 34 b/w illustrations

  • Topics: Pattern Recognition

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