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Viewpoint Tracking for 3D Display Systems
A look at the system proposed by
Yusuf Bediz, Gözde Bozdağı Akar
Outline
Why viewpoint tracking? Proposed System
Object Detection Tracking Viewpoint Calculation
Results Improvements Conclusion
Object Detection Overview
Training based on positive images of several peoples faces, negative images of backgrounds
Uses suite of linear classifier such as svms to classify ‘features’ such as eyes or face
Makes use of Adaboost to perform several re-weighted simple linear classifiers
Reducing Dimensionality
Two Rectangle MethodLossy CompressionRGB to GrayscaleResolution of the Image Reduction
Object Detection - Classification
Windows of an image are selected and resized2 Rectangle calculation is performedResult is passed into a SVM for ClassificationCascading classification occurs on all windows.
Results
95+% Accuracy on detection 80-90 ms for detection phase 7-8 ms for tracking phase Overall system runs at 20 fps
Improvements
Allow Y as well as X variation in viewpoint Improved tracking algorithm Improve framerate
References
Lucas B D and Kanade T 1981, An iterative image registration technique with an application to stereo vision. Proceedings of Imaging understanding workshop, pp 121--130
J.-Y. Bouguet. “Pyramidal implementation of the Lucas Kanade feature tracker”, OpenCV Documentation, Microprocessor Labs, Intel Corp., 2000
Toyama, K. and Hager, G. D. 1999. Incremental Focus of Attention for Robust Vision-Based Tracking. Int. J. Comput. Vision 35, 1 (Nov. 1999), 45-63. DOI= http://dx.doi.org/10.1023/A:1008159011682
Paul Viola , Michael Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features” 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 1
Qiong Wang, Jingyu Yang, and Wankou Yang, “Face Detection using Rectangle Features and SVM”, International Journal of Intelligent Technology Volume 1 Number 3, 2006
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