View-Invariant Human Action Detection Using Component-Wise HMM of Body Parts

  • Bhaskar Chakraborty
  • Marco Pedersoli
  • Jordi Gonzàlez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5098)

Abstract

This paper presents a framework for view-invariant action recognition in image sequences. Feature-based human detection becomes extremely challenging when the agent is being observed from different viewpoints. Besides, similar actions, such as walking and jogging, are hardly distinguishable by considering the human body as a whole. In this work, we have developed a system which detects human body parts under different views and recognize similar actions by learning temporal changes of detected body part components. Firstly, human body part detection is achieved to find separately three components of the human body, namely the head, legs and arms. We incorporate a number of sub-classifiers, each for a specific range of view-point, to detect those body parts. Subsequently, we have extended this approach to distinguish and recognise actions like walking and jogging based on component-wise HMM learning.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Bhaskar Chakraborty
    • 1
  • Marco Pedersoli
    • 1
  • Jordi Gonzàlez
    • 2
  1. 1.Computer Vision Center & Dept. de Ciències de la Computació, Edifici OCampus UABBellaterraSpain
  2. 2.Institut de Robòtica i Informàtica Industrial (UPC – CSIC) BarcelonaSpain

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