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A Real-time Particle Filtering Framework for Robust Camera Tracking in An AR Environment  

Lee, Seok-Han (중앙대학교 첨단영상대학원)
Publication Information
Journal of Digital Contents Society / v.11, no.4, 2010 , pp. 597-606 More about this Journal
Abstract
This paper describes a real-time camera tracking framework specifically designed to track a monocular camera in an AR workspace. Typically, the Kalman filter is often employed for the camera tracking. In general, however, tracking performances of conventional methods are seriously affected by unpredictable situations such as ambiguity in feature detection, occlusion of features and rapid camera shake. In this paper, a recursive Bayesian sampling framework which is also known as the particle filter is adopted for the camera pose estimation. In our system, the camera state is estimated on the basis of the Gaussian distribution without employing additional uncertainty model and sample weight computation. In addition, the camera state is directly computed based on new sample particles which are distributed according to the true posterior of system state. In order to verify the proposed system, we conduct several experiments for unstable situations in the desktop AR environments.
Keywords
Camera tracking; augmented reality; 3D reconstruction;
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