Markerless Visual Tracking for Augmented Books

An augmented book is an application that augments such multimedia elements as virtual 3D objects, movie clips, or sound clips to a real book using AR technologies. It is intended to bring additional education effects or amusement to users. For augmented books, this paper presents a markerless visual tracking method which recognizes the current page among numerous pages and estimates its 6 DOF pose in real-time. Given an input image by a camera, the tracking method first recognizes a page and performs wide-baseline keypoint matching at the same time. For that purpose,a generic randomized forest (GRF) is proposed which extends the randomized forest (RF) proposed by Lepetit et al. which only performs wide-baseline keypoint matching. The proposed GRF is capable of simultaneous page recognition and wide-baseline keypoint matching. Once a page is recognized, the tracking method executes the page tracking process without page recognition until the page is turned. The page tracking process selects a keyframe of the page adequate for tracking and employs a coarse-to-fine approach. As a result, the tracking method shows robustness to viewpoint and illumination variations and performance of more than 30 fps for augmented books.

[1]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[2]  Tom Drummond,et al.  Going out: robust model-based tracking for outdoor augmented reality , 2006, 2006 IEEE/ACM International Symposium on Mixed and Augmented Reality.

[3]  Axel Pinz,et al.  Robust Pose Estimation from a Planar Target , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[5]  Vincent Lepetit,et al.  The haunted book , 2008, 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality.

[6]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[7]  Ivan Poupyrev,et al.  The MagicBook - Moving Seamlessly between Reality and Virtuality , 2001, IEEE Computer Graphics and Applications.

[8]  Jiri Matas,et al.  Matching with PROSAC - progressive sample consensus , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Ian D. Reid,et al.  Real-Time SLAM Relocalisation , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[11]  Vincent Lepetit,et al.  Keypoint recognition using randomized trees , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Dieter Schmalstieg,et al.  Pose tracking from natural features on mobile phones , 2008, 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality.

[13]  V. Varadarajan Lie groups, Lie algebras, and their representations , 1974 .

[14]  Shogo Nishida,et al.  Virtual Pop-Up Book Based on Augmented Reality , 2007, HCI.

[15]  Mariano Alcañiz Raya,et al.  The memory book , 2005, ACE '05.

[16]  Kyusung Cho A Realistic E-Learning System based on Mixed Reality , 2007 .

[17]  Hyun Seung Yang,et al.  Hybrid Visual Tracking for Augmented Books , 2008, ICEC.

[18]  Vincent Lepetit,et al.  Monocular Model-Based 3D Tracking of Rigid Objects: A Survey , 2005, Found. Trends Comput. Graph. Vis..

[19]  G. Klein,et al.  Parallel Tracking and Mapping for Small AR Workspaces , 2007, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.

[20]  Olivier Stasse,et al.  MonoSLAM: Real-Time Single Camera SLAM , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.