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.
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Rhee, J.H., Sung, W.J., Nam, M.Y., Byun, H., Rhee, P.K. (2015). An Efficient Eye Tracking Using POMDP for Robust Human Computer Interaction. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_37
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