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Real-Time Upper Body Detection and 3D Pose Estimation in Monoscopic Images

  • Antonio S. Micilotta
  • Eng-Jon Ong
  • Richard Bowden
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3953)

Abstract

This paper presents a novel solution to the difficult task of both detecting and estimating the 3D pose of humans in monoscopic images. The approach consists of two parts. Firstly the location of a human is identified by a probabalistic assembly of detected body parts. Detectors for the face, torso and hands are learnt using adaBoost. A pose likliehood is then obtained using an a priori mixture model on body configuration and possible configurations assembled from available evidence using RANSAC. Once a human has been detected, the location is used to initialise a matching algorithm which matches the silhouette and edge map of a subject with a 3D model. This is done efficiently using chamfer matching, integral images and pose estimation from the initial detection stage. We demonstrate the application of the approach to large, cluttered natural images and at near framerate operation (16fps) on lower resolution video streams.

Keywords

Mixture Model Body Part Face Detection False Detection Integral Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Antonio S. Micilotta
    • 1
  • Eng-Jon Ong
    • 1
  • Richard Bowden
    • 1
  1. 1.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUnited Kingdom

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