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A Real-Time System for Head Tracking and Pose Estimation

  • Zengyin Zhang
  • Minyoung Kim
  • Fernando de la Torre
  • Wende Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6553)

Abstract

Driver’s visual attention provides important clues about his/ her activities and awareness. To monitor driver’s awareness, this paper proposes a real-time person-independent head tracking and pose estimation system using a monochromatic camera. The tracking and head-pose estimation tasks are formulated as regression problems. Three regression methods are proposed: (i) individual mapping on images for head tracking, (ii) direct mapping to subspace for head tracking, which predicts a subspace from one sample, and (iii) semantic piecewise regression for head-pose estimation. The approaches are evaluated on standard databases, and on several videos collected in vehicle environments.

Keywords

Support Vector Regression Ridge Regression Active Appearance Model Face Tracking Head Tracking 
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 2012

Authors and Affiliations

  • Zengyin Zhang
    • 1
  • Minyoung Kim
    • 1
  • Fernando de la Torre
    • 1
  • Wende Zhang
    • 2
  1. 1.Robotics InstituteCarnegie Mellon UniversityUSA
  2. 2.Electrical & Controls Integration LabGeneral Motors R&DUSA

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