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An Efficient Eye Tracking Using POMDP for Robust Human Computer Interaction

  • Ji Hye Rhee
  • Won Jun Sung
  • Mi Young Nam
  • Hyeran Byun
  • Phill Kyu Rhee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9163)

Abstract

We propose an adaptive eye tracking system for robust human-computer interaction under dynamically changing environments based on the partially observable Markov Decision Process (POMDP). In our system, real-time eye tracking optimization is tackled using a flexible world-context model based POMDP approach that requires less data and time in adaptation than those of hard world-context model approaches. The challenge is to divide the huge belief space into world-context models, and to search for optimal control parameters in the current world-context model with real-time constraints. The offline learning determines multiple world-context models based on image-quality analysis over the joint space of transition, observation, reward distributions, and an approximate world-context model is balanced with the online learning over a localized horizon. The online learning is formulated as a dynamic parameter control with incomplete information under real-time constraints, and is solved by the real-time Q-learning approach. Extensive experiments conducted using realistic videos have provided us with very encouraging results.

Keywords

Eye tracking POMDP Real-time Q-learning World-context model Image-quality analysis 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ji Hye Rhee
    • 1
  • Won Jun Sung
    • 2
  • Mi Young Nam
    • 3
  • Hyeran Byun
    • 1
  • Phill Kyu Rhee
    • 2
  1. 1.Department of Computer ScienceYonsei UniversitySeoulSouth Korea
  2. 2.Department of Computer Science & EngineeringInha UniversityIncheonSouth Korea
  3. 3.YM-NaeultechIncheonSouth Korea

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