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The Bag of Micro-Movements for Human Activity Recognition

  • Pejman HabashiEmail author
  • Boubakeur Boufama
  • Imran Shafiq Ahmad
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9164)

Abstract

The bag of words is a popular and successful method for human activity recognition. This method usually uses visual based sparse features for activity classification. It is also known that movement has useful clues for activity detection, but sparse features usually miss this vital piece of information. Two-dimensional image planar motion information is easy to extract but it is very dependant on depth and calibration parameters. Three-dimensional motion is rich in information and can be calculated from active cameras or multiple passive cameras, but it restricts the applicability of the method. To overcome these issues, we have proposed the use of disparity maps, which are relatively easy to extract from stereo videos and are more informative than 2D image planar motion information. In this work, we have combined the motion information and disparity maps to introduce a new sparse feature descriptor that encodes motion information, instead of visual information.

Keywords

Micro-movements Feature descriptor Motion-based descriptor Human Activity Recognition Machine vision 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Pejman Habashi
    • 1
    Email author
  • Boubakeur Boufama
    • 1
  • Imran Shafiq Ahmad
    • 1
  1. 1.Computer Science DepartmentUniversity of WindsorOntarioCanada

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