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Automatic confidence adjustment of visual cues in model‐based camera tracking
Author(s) -
Park Hanhoon,
Oh Jihyun,
Seo ByungKuk,
Park JongIl
Publication year - 2010
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.321
Subject(s) - computer science , computer vision , artificial intelligence , pixel , tracking (education) , video tracking , object (grammar) , feature (linguistics) , augmented reality , camera resectioning , psychology , pedagogy , linguistics , philosophy
Model‐based camera tracking is a technology that estimates a precise camera pose based on visual cues (e.g., feature points, edges) extracted from camera images given a 3D scene model and a rough camera pose. This paper proposes an automatic method for flexibly adjusting the confidence of visual cues in model‐based camera tracking. The adjustment is based on the conditions of the target object/scene and the reliability of the initial or previous camera pose. Under uncontrolled or less‐controlled working environments, the proposed object‐adaptive tracking method works flexibly at 20 frames per second on an ultra mobile personal computer (UMPC) with an average tracking error within 3 pixels when the camera image resolution is 320 by 240 pixels. This capability enabled the proposed method to be successfully applied to a mobile augmented reality (AR) guidance system for a museum. Copyright © 2009 John Wiley & Sons, Ltd.