A Robust Integrated Framework for Segmentation and Tracking

  • Prabhu Kaliamoorthi
  • Ramakrishna Kakarala
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8333)

Abstract

Recent studies on human motion capture (HMC) indicate the need for a likelihood model that does not rely on a static background. In this paper, we present an approach to human motion capture using a robust version of the oriented chamfer matching scheme. Our method relies on an MRF based segmentation to isolate the subject from the background, and therefore does not require a static background. Furthermore, we use robust statistics and make the likelihood robust to outliers. We compare the proposed approach to the alternative methods used in recent studies in HMC using the Human Eva I dataset. We show that our method performs significantly better than the alternatives despite of not assuming a static background.

Keywords

Tracking Error Motion Capture Canny Edge Lighting Change Foreground Region 
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 2014

Authors and Affiliations

  • Prabhu Kaliamoorthi
    • 1
  • Ramakrishna Kakarala
    • 1
  1. 1.Nanyang Technological UniversitySingapore

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